Ericsson Technology Review - Issue 1, 2018

ERICSSON
TECHNOLOGY
C H A R T I N G T H E F U T U R E O F I N N O V A T I O N | V O L U M E 9 6 I 2 0 1 8 – 0 1
MACHINEINTELLIGENCE
&INDUSTRIAL
AUTOMATION
IOTSECURITY
MANAGEMENT
5GNR
PHYSICALLAYER

✱ XXXXXXXXXXX XXXXXXXXXX ✱
2 #01 2018 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ #01 2018 3

#01 2018 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ #01 2018 5
CONTENTS ✱
08 VIDEO QoE: LEVERAGING STANDARDS TO MEET
RISING USER EXPECTATIONS
Mobile network operators and service providers stand to gain a great deal
from developing a better understanding of how users experience video quality.
The implementation of the ITU-T P.1203 and 3GPP TS 26.247 standards is the best
way to enable the efficient and accurate estimation of video QoE required to
meet continuously rising user expectations.
18 DESIGNINGFORTHEFUTURE:THE5GNRPHYSICALLAYER
Flexibility, ultra-lean design and forward compatibility are the pillars on
which all the 5G New Radio physical layer technology components are
being designed and built. The high level of flexibility and scalability in 5G NR
will enable it to meet the requirements of diverse use cases, including a wide
range of carrier frequencies and deployment options.
30 5G NETWORK PROGRAMMABILITY FOR MISSION-CRITICAL
APPLICATIONS
5G will make it possible for mobile network operators to support enterprises in a
wide range of industry segments by providing cellular connectivity to mission-critical
applications. The ability to expose policy control to enterprise verticals will create
significant new business opportunities for mobile network operators.
52 ENABLING INTELLIGENT TRANSPORT IN 5G NETWORKS
The diverse requirements of a growing set of use cases in the areas of enhanced
mobile broadband, ultra-reliable low-latency communication and massive machine-
type communications continue to drive the evolution toward 5G. Meeting these
requirements in a cost-efficient manner will require enhancements not only to
RAN and mobile core networks, but also to the transport network, which links
all the pieces together.
62 END-TO-END SECURITY MANAGEMENT FOR THE IoT
Service providers that want to capitalize on IoT opportunities without taking
undue risks need a security solution that provides continuous monitoring of
threats, vulnerabilities, risks and compliance, along with automated
remediation. We have developed an end-to-end IoT security and identity
management architecture that delivers on all counts.
FEATURE ARTICLE
Industrial automation enabled by
robotics, machine intelligence and 5G
The emergent fourth industrial revolution will have a profound impact on
both industry and society in the years ahead. Together with Comau and
Telecom Italia Mobile (TIM), we have created a proof of concept (PoC) and
tested it in a real industrial environment. The results are promising, suggesting
that our PoC has the potential to become a key element in the factory of the future.
40
Control and observability
Management and orchestration
TIF
Core
Regional data center
MetroAccess
HighLow latency / reliability
Low latency / reliability
Central officeHub site
Core
DU
CU
CoreCU
SWRF
GW
GW
GW
GW
GW
GWGW
GW
GW
GWGW
URLLC
URLLC
URLLC
eMBB
eMBB
52
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62
08

EDITORIAL ✱✱ EDITORIAL
Ericsson Technology Review brings you
insights into some of the key emerging
innovations that are shaping the future of ict.
Our aim is to encourage an open discussion
about the potential, practicalities, and benefits
of a wide range of technical developments,
and provide insight into what the future
has to offer.
a d d r e s s
Ericsson
se-164 83 Stockholm, Sweden
Phone: +46 8 719 00 00
p u b l i s h i n g
All material and articles are published on the
Ericsson Technology Review website:
www.ericsson.com/ericsson-technology-review
p u b l i s h e r
Erik Ekudden
e d i t o r
Tanis Bestland (Nordic Morning)
tanis.bestland@nordicmorning.com
e d i t o r i a l b o a r d
Håkan Andersson, Anders Rosengren,
Mats Norin, Erik Westerberg,
Magnus Buhrgard, Gunnar Thrysin,
Peter Öhman, Håkan Olofsson, Dan Fahrman,
Robert Skog, Patrik Roseen, Jonas Högberg,
John Fornehed and Sara Kullman
f e at u r e a r t i c l e
Industrial automation enable by robotics,
machine intelligence and 5G by Roberto
Sabella, Andreas Thuelig, Maria Chiara
Carrozza and Massimo Ippolito
a r t d i r e c t o r
Liselotte Eriksson (Nordic Morning)
p r o d u c t i o n l e a d e r
Susanna O’Grady (Nordic Morning)
l ay o u t
Liselotte Eriksson (Nordic Morning)
i l l u s t r at i o n s
Nordic Morning Ukraine
c h i e f s u b e d i t o r
Ian Nicholson (Nordic Morning)
s u b e d i t o r s
Paul Eade and Penny Schröder-Smith
(Nordic Morning)
issn: 0014-0171
Volume: 96, 2018
■ we are publishing this magazine shortly after
the first release of 5G from 3GPP, which is significant
because the first release of a completely new standard
only happens every 10 years. Fittingly, many of the
articles in this issue relate to what we think is most
important in 5G and how to address the new
opportunities that 5G entails.
Defining New Radio (NR) was a key focus in the first
release of 5G. While the development of the radio
for a new generation has traditionally focused on
the introduction of a new modulation and coding
scheme, the main focus this time has been on
flexibility to support a large range of devices and
services with vastly different characteristics,
different types of deployments and frequency
allocations that range from below 1 GHz well up into
the mmWave bands. To support the expected
growth in data volumes, innovative technologies in
the area of massive antenna systems, beamforming
and energy efficiency have been introduced.
One of the key reasons for the flexibility provided in 5G
is the desire to support industries to use connectivity,
virtualization, machine intelligence and other
technologies to change their processes and business
models as part of the next industrial revolution,
Industry 4.0. It is therefore a pleasure to be able to
include an article in this issue that we have co-written
with Comau and the Sant’Anna School of Advanced
Studies on the topic of industrial automation.
In this issue we also cover the topic of 5G network
programmability, which allows the network
5G, IoT AND THE
NEXT INDUSTRIAL
REVOLUTION
platform to support a wide range of applications,
including the mission critical applications that
are required to support industrial automation.
We have also included an article on the subject
of intelligent transport that describes a transport
network solution that connects all the pieces of
the RAN and the mobile core network, and where
highlevelsofintelligence,flexibilityandautomation
are used to provide optimal performance for a
variety of different 5G scenarios.
We know that the protection of both networks and
dataisakeyrequirementforanyenterpriseorindustry
that uses LTE and 5G networks. We address this
topic in an article that looks specifically at end-to-
end (E2E) security management for the Internet
of Things (IoT).
We also know that video is sure to play in
important role in many 5G applications.
With the increased consumption of video over
mobile networks, it is crucial to ensure a
high-quality viewing experience. In light of this,
Ericsson has been part of standardizing a model
for measuring viewing quality, which we present
here in an article about video QoE (Quality of
Experience).
With the first 5G standard released and commercial
deployments starting this year, we are entering a
new era of mobile communications with the potential
to profoundly change the way that many businesses
and industries operate. This change may well be
similar to the way that the introduction of mobile
phones changed the way people interact both with
each other and with different applications. We will
continue to explore the possibilities of 5G in future
ETR articles.
If you would like to see the contents of this
magazine in digital form, you can find all of the
articles, along with those published in previous
issues, at: www.ericsson.com/ericsson-
technology-review
WE ARE ENTERING A NEW ERA OF MOBILE
COMMUNICATIONS WITH THE POTENTIAL
TO PROFOUNDLY CHANGE THE WAY THAT MANY
BUSINESSES AND INDUSTRIES OPERATE
ERIK EKUDDEN
SENIOR VICE PRESIDENT,
GROUP CTO AND
HEAD OF TECHNOLOGY & ARCHITECTURE

VIDEO QUALITY OF EXPERIENCE ✱✱ VIDEO QUALITY OF EXPERIENCE
AdaptiveHTTP-basedstreaming
AdaptiveHTTP-basedvideostreamingvariants
suchasDASHandHLSarethedominantvideo
deliverymethodusedtoday.Theirstreaming
serversprovideseveralversionsofthesamevideo,
eachseparatelyencodedtooffervaryinglevelsof
quality.Thestreamingclientintheuser’sphone,
tabletorPCdynamicallyswitchesbetweenthe
differentversionsduringplayoutdependingonhow
muchnetworkbitrateisavailable.
Iftheavailablenetworkbitrateishigh,theclient
willdownloadthebest-qualityversion.Ifthebitrate
suddenlydrops,theclientwillswitchtoalower-
qualityversionuntilconditionsimprove.The
purposeofthisistoavoidstalling(whentheclient
stopsplayoutintermittentlytofillitsvideobuffer)–
whichisknowntoannoyusers.
Whiletheabilitytoswitchintermittentlybetween
versionsofavideosignificantlydecreasestheriskof
stalling,thequalityvariationsthatthisleadstocan
alsobeannoyingfortheuser.Figure1providesan
exampleofaworst-casescenariowithbothquality
variationsandastallingevent,whichwouldresult
inanoveralllow-qualityexperiencefortheuser.
Subjectivequality
ITU-T P.910 [3] is the recognized standard for
performing subjective video quality tests. The
tests are carried out in a lab equipped with
mobile phones, tablets, PCs or TVs, where
a number of videos are shown to a group of
individuals. Each individual then grades each
video according to their subjective perception of
its quality, selecting one of the following scores:
5 (excellent), 4 (very good), 3 (good), 2 (fair) or 1
(poor). Finally, the average score for each video
is calculated. This number is known as the mean
opinion score (MOS).
The video sequences are typically produced
in a lab environment so that all types of
impairments can be included. High-quality
sports, nature and news videos are used as a
starting point. Impairments (codec settings,
rate and resolution changes, initial buffering,
stalling and so on) are then emulated by varying
the bitrate over a certain range, for example,
or placing stalling events of different lengths at
various points in the videos.
Thetestsaredesignedtomakeanalysisas
accurateandstraightforwardaspossible.Devising
subjectivetestsisatimeconsumingandexpensive
process,though,andlabtestscan’tassessexactly
whatanMNO’srealusersareexperiencing.The
bestwaytoovercomethesechallengesisbyusing
objectivequalityalgorithms.
THE TESTS ARE
DESIGNED TO MAKE
ANALYSIS AS ACCURATE
AND STRAIGHTFORWARD
AS POSSIBLE
Terms and abbreviations
APN–AccessPointName|AVC–advancedvideocoding|DASH–DynamicAdaptiveStreamingoverHTTP|
DM–devicemanagement|eNB–eNodeB(LTEbasestation)|ETSI–EuropeanTelecommunicationsStandards
Institute|HD–highdefinition|HEVC–HighEfficiencyVideoCoding|HLS–HTTPLiveStreaming|HTTP–Hypertext
TransferProtocol|ITU-T–InternationalTelecommunicationUnionTelecommunicationStandardizationSector|
MNO–mobilenetworkoperator|MOS–meanopinionscore|MPD–mediapresentationdescription|MSP–media
serviceprovider|OMA–OpenMobileAlliance|Pa–short-termaudiopredictor|Pq–long-termqualitypredictor|
Pv–short-termvideopredictor|QoE–qualityofexperience|RRC–RadioResourceControl|SMS–shortmessage
service|UHD–ultrahighdefinition|VOD–videoondemand|VP9–Anopen-sourcevideoformatandcodec
GUNNAR HEIKKILÄ,
JÖRGEN GUSTAFSSON
In 2016, Ericsson ConsumerLab found that
the average person watches 90 minutes
more TV and video every day than they did
in 2012 [1]. While traditionally broadcast
linear TV is still popular with many viewers,
internet-based TV and video delivery and
video on demand (VOD) services are all
growing rapidly. In fact, 20 percent of video
consumption occurred on handheld devices
in 2016 [1].
■Asusersbecomemoreaccustomedtovideo
streamingservices,theirqualityexpectations
rise,presentingabigchallengeformediaservice
providers(MSPs)andmobilenetworkoperators
(MNOs).Whiledeliveringhigh-qualityvideo
overafixedconnectioncanbedifficult,doingso
wirelesslyisamuchmoredemandingundertaking.
Yetby2022,75percentofallmobiledatatrafficis
expectedtocomefromvideo,accordingtothe2016
EricssonMobilityReport[2].
At the same time, TV screens are getting
larger, which requires higher video resolution.
High definition (HD) is now the new baseline,
and ultra high definition (UHD) is coming to
both fixed and mobile devices, demanding higher
bandwidth. To provide a consistently high QoE
– particularly in wireless cases with significant
fluctuations in available bandwidth – both MSPs
and MNOs must have a clear understanding of
which impairments their users are experiencing
and be able to accurately assess their perception
of video quality.
‘How happy are our users with their video experience?’ has become a vital
question for mobile network operators and media service providers alike.
New standards for QoE testing have the potential to help them ensure that
they are able to meet user expectations for the service that will account for
three-quarters of mobile network traffic in five years’ time.
VideoQoELEVERAGING STANDARDS
TO MEET RISING USER EXPECTATIONS

Objectivequality
Asthewordingsuggests,objectivequality
algorithms(alsoknownasobjectivemodels)are
designedtomimicthebehaviorandperceptionof
humans.Thegoalistoproducethesamescoresas
theMOSvaluesthatwouldresultfromrunninga
subjectivetestonthesamevideos.
Manydifferenttypesofobjectivemodelscan
beadopted,dependingontheintendedusage
andthekindofinputdataemployed.Thoseusing
themostlimitedsetofinputparametersbasethe
objectivequalityestimationonencodingrates,
videoresolution,framerates,codecsandstalling
information,asthesefactorsprovidetheminimum
amountofinformationaboutthevideoplayout
thatisrequiredtoestimateaqualityscore.More
complexmodelsmightusethecompleteencoded
videobitstream,oreventhefullreceivedvideo
signal,tofurtherincreasetheestimationaccuracy.
Theobjectivemodelsdescribedaboveare
no-referencemodels,whereinputistakenonly
fromthereceivingendofthemediadistribution
chain.Full-referencemodelscanalsobeadopted,
wherethevideooriginallytransmittedis
comparedwiththeonethatisreceived.Another
variantisthereduced-referencemodel,where
theoriginalvideoisnotneededforreference,but
certaininformationaboutitismadeavailableto
themodel.
Traditionally,objectivemodelsareusedto
evaluatequalitybasedonrelativelyshortvideo
sequences:approximately10secondslong.
However,withadaptivevideostreaming,where
qualitycanvarysignificantlyduringagiven
session,themodelmustalsoassesshowthislong-
termvariationaffectsuserperception.Todothis,
modelevaluationofmuchlongervideosequences
(uptoseveralminutes)isrequired.
Ideally,differentlevelsofcomplexityshouldbe
usablebyasinglemodel–thatis,fromonlyafew
inputparametersuptothefullbitstream,depending
ondeployment.Thisisthescopethatapplieswith
thenewno-referenceITU-TP.1203standard.
StandardizationofITU-TP.1203
TheP.1203standard[4]wasdevelopedaspart
ofanITU-Tcompetitionbetweenparticipating
proponents(Ericssonandsixothercompanies),
whereeachonesentinitsproposedquality
models.Themodelsthatperformedbestwere
thenusedasabaselinetocreatethefinalstandard.
Aninternalmodelarchitecturewasalsodefinedto
facilitatethecreationofamodelthatwouldbeas
flexibleaspossible.
Architecture
Thestandardincludesmodulesforestimatingshort-
termaudioandvideoquality,andanintegration
moduleestimatingthefinalsessionqualitydueto
adaptationandstalling,asshowninFigure2.
Theshort-termvideoandaudiopredictor(Pv
andPa)modulescontinuouslyestimatetheshort-
termaudioandvideoqualityscoresforone-second
piecesofcontent.Thismeansthatfora60-second
video,therewillbe60audioscoresand60video
scores.ThesePvandPamodulesarespecificto
eachtypeofcodec.
ThePvandPamodulesoperateinuptofour
differentmodes,dependingonhowdetailedthe
inputisfromtheparameterextraction.Forthe
leastcomplexmode,themaininputsarerelatedto
resolution,bitrateandframerate,whilethemost
complexmodeperformsadvancedanalysisofthe
videopayload.
Theshort-termscoresfromthosemodules
arefedintothelong-termqualitypredictor(Pq)
module,togetherwithanystallinginformation,
andthefinalsessionqualityscoreforthetotal
videosessionisthenestimated.ThePqmodule
alsoproducesanumberofdiagnosticoutputs,
sothattheunderlyingcausesofthescorecanbe
analyzed.ThePqmoduleisnotmode-orcodec-
dependentandisthereforecommonforallcases.
IDEALLY, DIFFERENT
LEVELS OF COMPLEXITY
SHOULD BE USABLE BY A
SINGLE MODEL
Figure 1 A 60-second video with quality variations (resolution) and a three-second stall in the middle
854x480
50403020100 60 seconds
Stalling426x2401920x1080

Training
InthedevelopmentandstandardizationofP.1203,
alargenumberofsubjectivetestdatabaseswere
created,eachcontainingvideosthatweregraded
byatleast24testindividuals.Animportantgoal
setinthedevelopmentofP.1203wastohandlethe
long-termperceivedeffectsofstallingeventsand
qualityadaptations.
Thus,incontrasttotraditionalsubjectivetests
inwhichafew10-secondvideosaretypically
repeated,thevideosusedforP.1203wereall
uniqueandbetweenoneandfiveminutes
long.Itwasimportanttoavoidrepetitionand
continuouslypresentnewtestvideosthatthe
viewersfoundinterestingenoughtopayattention
tothroughouttheplayout.Thetestsweredoneon
mobiledevicesaswellasoncomputermonitors
andTVscreens,tocoverallthedifferentusecases.
Validation
SincethedevelopmentoftheP.1203standardization
wasrunasacompetition,theperformanceofthe
differentproponentmodelsrequiredvalidation,
andthevalidationcouldnotbecarriedoutonany
existingdatabases.Instead,afterthesubmissionof
alloftheproponents’candidatemodelstoITU-T,
alltheproponentsworkedtogethertodesigna
newsetofdatabases,witheachstepdistributed
todifferentproponentssothatnonehadcomplete
controloveranyindividualdatabase.
Thenewsetofvalidationdatabaseswere
thenusedtoevaluatethemodels,andthetop-
performingoneswereselectedtoformthe
P.1203standard.Incaseswheretherespective
performanceofseveralproponentmoduleswasso
closethatastatisticaltestcouldnottellthemapart,
themodulesweremergedtoformasinglestandard
withoutalternativeimplementations.
Finalmodel
WhilethePvandPamodulesweredevelopedusing
traditionalanalyticalmethodsandimplementedas
aseriesofmathematicalfunctions,thePqmodule
ismoreadvanced.Thismoduleisdividedinto
twoseparateestimationalgorithms:oneusing
atraditionalfunctionalapproachandtheother
basedonmachine-learningconcepts.
Thefunctionalvariantmodelshuman
perception,asinfluencedbytheeffectofquality
oscillations,deepqualitydips,repeatedquality
orstallingartifactsandtheabilitytomemorize.
Alloftheseeffectsaredescribedbymathematical
functions(astheyareforthePvandPamodules),
whicharethencombinedtoestimatetheuser’stotal
perceptionofquality.
Machinelearningisamethodthatsolvesa
problemwiththesupportofself-learningcomputer
algorithms.Itiswellsuitedforproblemswherethe
relationshipbetweentheinputandtheoutputis
complex,asinthePqmodule.Duringthedesign
andtrainingphase,thealgorithmsautomatically
identifyhowvariouscharacteristicsoftheinput
data(Pv/Pascoresandstallingparameters)
arereflectedinchangestotheoutputdata(the
testpanelMOSvalues).Thealgorithmthen
automaticallybuildsablack-boxalgorithm,which
implementsthefinalmachine-trainedsolutionand
estimatestheuserscore.
ThefinalPqMOSestimateisaweightedaverage
oftheoutputfromthetraditionalfunctional
algorithmandthemachine-learning-basedone.
OneoftheadvantagesofusingtwodifferentPq
algorithmsisthattheyhavestatisticallyindependent
estimationerrors,andwhenthetwoscoresare
averaged,theactualerrorbecomessmaller.
Futurestandardizationwork
ThevideomodulecurrentlysupportsH.264/
AVCvideocodecsuptoHD(1920x1080).Anew
workitemhasbeenstartedinITU-T,whichwill
resultinarecommendationthatalsosupports
H.265/HEVCandVP9videocodecsuptoUHD
resolution(3840x2160).Thisworkitemisrunning
inasimilarfashiontoP.1203,withacompetition
givingparticipatingcompaniestheopportunityto
submittheirownproposedmodels.
Figure 2 ITU-T P.1203 architecture
Video
stream
MOS
Diagnostic
output #1
Diagnostic
output #2
Diagnostic
output #3
Diagnostic
output #4
P.1203
Media
parameter
extraction
Pv: Short-term video
quality predictor
module (P.1203.1)
Pa: Short-term audio
quality predictor
module (P.1203.2)
Pq: Long-term
quality
predictor
module
(P.1203.3)
Stalling
parameter
extraction

Figure 3 Example of a video QoE microservice
Implementingaqualitymodel
Successfulimplementationofaqualitymodelis
dependentonaccesstotheinputdatarequiredby
themodelitself.Themostdemandingmodels,such
asfull-referencevariants,areusuallyimplemented
closetothevideostreamingclient,insidethedevice,
sothatthecompletereceivedvideocanbecompared
withtheonesent.Thisistypicallydoneformanual
testingscenarios,whereaspecialtestphoneisused
inwhichaqualitymodelhasbeenimplemented.
Thismethodisnotfeasibleforpassive
measurements,wherealloralargepartoflivevideo
trafficismonitored,somodelinputdataneedsto
becollectedinanotherway.Forexample,anMNO
thatwantstogainabetterunderstandingofits
overallperceivedvideostreamingqualitywould
needtocollectdatafromallstreamingsessions.
Onewayofdoingthiswouldbetointerceptthe
trafficatcertainnetworknodesandusethetraffic
contentandpatterntotrytoinferwhichserviceis
beingusedandthequalitylevelbeingdelivered.
Thiscanbedifficult,however,owingtothefact
thatmanyservicesarenowencrypted,which
significantlylimitsaccesstothedatarequiredtodo
adetailedqualityestimation.
Onewaytoovercomethischallengeiswiththe
helpofthestreamingclient,whichhasfullknowledge
ofwhatishappeningduringthevideosession.A
feedbacklinkfromthestreamingclientcanbeused
toreportselectedmetricstothenetwork(orthe
originalstreamingserver)wherethequalitycanbe
estimated.Thistechniqueisalreadyusedinternally
formanystreamingservices.Forexample,whena
userclicksonavideolinkontheinternet,theclient
typicallycontinuouslymeasuresdifferentmetrics
andsendsthemtotheserver.Unfortunatelyfor
MNOs,though,thesefeedbackchannelsareusually
encryptedandavailableonlytotheMSP.Evenif
anMSPweretomakethemetricsavailabletothe
MNO,theywouldstillbeproprietary,anditwould
bedifficultfortheMNOtocomparethemduetothe
factthatthecontentandlevelofdetailtypicallydiffer
betweenMSPs.
3GPPQoEreporting
Theusefulnessofhavingastandardizedclient
feedbackmechanismhasbeenrecognizedby3GPP,
anditstechnicalspecificationTS26.247describes
howthiscanbeimplementedforaDASHstreaming
client[5].The3GPPconceptiscalledQoEreporting,
asthemetricscollectedandreportedarerelated
specificallytothequalityofthesession.Sensitiveor
integrity-relateddatasuchastheuser’spositionand
thecontentviewedcannotbereported.
Thebasicconceptisthatthestreamingclient
canreceiveaQoEconfigurationthatspecifiesthe
metricstobecollected,howoftencollectionwill
occur,whenreportingwillbedoneandwhichentity
toreportto.TherearethreewaystosendaQoE
configurationtotheclienttofacilitatedifferent
deploymentcases:
1.Mediapresentationdescription
Inthiscase,theclientdownloadsamedia
presentationdescription(MPD)whenstreaming
starts.TheMPDspecifieshowthemediais
structuredandhowtheclientcanaccessand
downloadthemediachunks.TheMPDcanalso
containaQoEconfigurationthatmakesitpossible
togetfeedbackfromtheclient.SincetheMPD
isusuallycontrolledbythecontentorservice
provider,theQoEreportsfromtheclientare
typicallyconfiguredtogobacktotheirserversand
arenotalwaysvisibletotheMNO.
2.OpenMobileAllianceDeviceManagement
OpenMobileAllianceDeviceManagement
(OMA-DM)hasdefinedmethodsforhowan
MNOcanconfigurecertainaspectsofconnected
devicessuchasAPNs,SMSserversandsoon.
ThesemethodsalsoincludeanoptionalQoE
THE USEFULNESS OF
HAVING A STANDARDIZED
CLIENT FEEDBACK
MECHANISM HAS BEEN
RECOGNIZED BY 3GPP
Video QoE microservice instance
CoreOS – guest operating system
Video player Player events
Video QoE app
Tomcat
Debian run time
Debian run time
App metrics
collection
Metrics
generation
Load
balancer
Debian run time
Debian run time
Debian run time
KafkaDevice
(Android, iOS, ...)
Media manager
Service docker Monitor docker
Collected docker
Filebeat docker
Logrotate docker
Video QoE estimation service
Raw player
events
transmitted
over network
Video QoE
Video ingestion profiles
Logging server
Video QoE calculation
Initiation and coordination
Ingestion profile

Gunnar Heikkilä
◆ is a senior specialist in
machine intelligence at
Ericsson Research. Since
1996, he has focused
on user experience
quality assessment and
measurements, including
standardization in 3GPP,
ETSI and ITU-T. He joined
Ericsson in 1987 and has
previously worked with
control system software
for military defense radar
systems, and with software
design for synchronous
digital hierarchy optical
fiber transmission systems.
Heikkilä holds an M.Sc.
in computer science
from Luleå University of
Technology, Sweden.
Jörgen Gustafsson
◆ is a research manager at
Ericsson Research, heading
a research team in the areas
of machine learning and
QoE. The research is applied
to a number of areas, such as
media, operations support
systems/business support
systems, the Internet of
Things and more. He joined
Ericsson in 1993. He is
co-rapporteurofQuestion
14 in ITU-T Study Group
12, where leading and
global standards on
parametric models and
toolsformultimediaquality
assessment are being
developed, including the
latest standards on quality
assessment of adaptive
streaming.HeholdsanM.Sc.
in computer science from
LinköpingUniversity,Sweden.
theauthors
1. EricssonConsumerLabreport,TVandMedia2016,availableat:https://www.ericsson.com/networked-
society/trends-and-insights/consumerlab/consumer-insights/reports/tv-and-media-2016
2. EricssonMobilityReport,November2016,availableat:https://www.ericsson.com/en/mobility-report
3. ITU-TP.910,April2008,Subjectivevideoqualityassessmentmethodsformultimediaapplications,availableat:
http://www.itu.int/itu-t/recommendations/rec.aspx?rec=9317
4. ITU-TP.1203,November2016,Parametricbitstream-basedqualityassessmentofprogressive
downloadandadaptiveaudiovisualstreamingservicesoverreliabletransport,availableat:
http://www.itu.int/itu-t/recommendations/rec.aspx?rec=13158
5. 3GPP TS 26.247, January 2015, Transparent end-to-end Packet-switched Streaming Service (PSS);
Progressive Download and Dynamic Adaptive Streaming over HTTP (3GP-DASH), available at:
http://www.3gpp.org/DynaReport/26247.htm
6. 3GPP TS 25.331, January 2016, Radio Resource Control (RRC); Protocol specification, available at:
http://www.3gpp.org/DynaReport/25331.htm
References:
configurationthatactivatesQoEreportingfrom
theclient.However,notallMNOsdeployOMA-
DMintheirnetworks.
3.RadioResourceControl
TheRadioResourceControl(RRC)protocol[6]
isusedbetweentheeNBandthemobiledevice
tocontrolthecommunicationintheRAN.The
possibilityofincludingaQoEconfigurationwas
addedin3GPPrel-14,givingMNOstheabilityto
useRRCtoactivateQoEreporting.Asaresult,
theQoEconfigurationcanbehandledlikemany
othertypesofRAN-relatedconfigurationsand
measurements.
TheQoEmetricsreportedbytheclientineach
ofthesethreemethodsarewellalignedwiththe
inputrequirementsinP.1203.Thismeansthat,
inprinciple,anyofthemwouldenableanMNO
togainagoodoverviewofthevideostreaming
qualityexperiencedbyitsusers.Beforethiscan
happen,though,thenewstandardswillneedto
bedeployedbothinthenetworkandintheclients.
DeploymentofvideoQoEestimation
Thedeliveryofover-the-topvideotodaymost
commonlyusesacloud-basedmicroservices
platformwithinbuiltvideo-qualityestimation
features.TheP.1203algorithmforestimatingvideo
qualityismostsuitableforimplementationasa
microservicethatestimatesvideoquality(interms
ofMOS)forallvideostreamsandforallindividual
videostreamingsessions.Iftheestimatedvideo
qualitydistributionshowsthatthereisaquality
issue,arootcauseanalysiscanbecarriedoutandthe
necessarymeasurescanbetakentoimprovequality.
Whenrunningamediaservice,andproviding
thevideoclientaspartoftheservice,theinput
parameterstotheP.1203algorithmaretakendirectly
fromthevideoclientthathasallthedetailsabout
theplayoutofthestreamandreportedoverthe
networktoananalyticsbackend.Figure3outlines
anexampleofsuchanimplementation,inwhich
playereventsarereportedtoastreamprocessing
system(Kafka)wherethevideoQoEiscalculated
inavideoQoEmicroservice.Theoutputfromthe
videoQoEcalculationisthenpostedbackintothe
streamprocessingsystemtobeusedformonitoring,
visualization,rootcauseanalysisandotherpurposes.
ForanMNO,thestandardarchitectureisto
installaprobeinsidethenetwork–inthecore
network,forexample.Theprobemonitorsvideo
trafficusingshallowordeeppacketinspection
andgathersinformationthatcanbeusedasinput
toaQoEestimationalgorithm(P.1203).Ifavideo
serviceisnotencrypted,therelevantmetrics
andeventsfromthevideostreamscanoftenbe
measuredorestimated.Thetaskbecomesmore
challengingifthevideostreamsareencrypted,but
qualityestimationsofthesevideostreamscanstill
bedonetosomeextentbyusingacombinationof
probesandstandardizedandproprietaryalgorithms
andmodels.However,theabilitytoreportquality-
relatedmetricsdirectfromthevideoclientsenables
muchmoreaccurateestimationofquality.
Conclusion
MNOs and MSPs stand to gain a great deal from
developing a better understanding of how users
experience video quality, and the ITU-T P.1203
and 3GPP TS 26.247 standards provides the
framework that is necessary to help them do so.
Implementation of these standards by MNOs,
MSPs and device manufacturers will enable
the efficient and accurate estimation of video
QoE required to meet continuously rising user
expectations. The standard’s smart handling of
theeffectsofstallingeventsandqualityadaptations
makes it well suited to overcome the challenges
presented by VOD and by adaptive video
streaming in particular.
THE INPUT PARA-
METERS TO THE P.1203
ALGORITHM ARE TAKEN
DIRECTLY FROM THE
VIDEO CLIENT

5G NR PHYSICAL LAYER DESIGN ✱✱ 5G NR PHYSICAL LAYER DESIGN
networksandtheadditionofanew,globally
standardizedradioaccesstechnologyknownas
NewRadio(NR).
Changeto5GNewRadio
5GNRwilloperateinthefrequencyrange
frombelow1GHzto100GHzwithdifferent
deployments.Therewilltypicallybemorecoverage
perbasestation(macrosites)atlowercarrier
frequencies,andalimitedcoverageareaperbase
station(microandpicosites)athighercarrier
frequencies.Toprovidehighservicequalityand
optimalreliability,licensedspectrumwillcontinue
tobethebackboneofthewirelessnetworkin5G,
andtransmissioninunlicensedspectrumwillbe
usedasacomplementtoprovideevenhigherdata
ratesandboostcapacity.Theoverallvisionfor5G
intermsofusecases,operatingfrequenciesand
deploymentsisshowninFigure1.
ThestandardizationofNRstartedin3GPPin
April2016,withtheaimofmakingitcommercially
availablebefore2020.3GPPistakingaphased
approachtodefiningthe5Gspecifications.A
firststandardizationphasewithlimitedNR
functionalitywillbecompletedby2018,followedby
asecondstandardizationphasethatfulfillsallthe
requirementsofIMT-2020(thenextgenerationof
mobilecommunicationsystemstobespecifiedby
ITU-R)by2019.ItislikelythatNRwillcontinueto
evolvebeyond2020,withasequenceofreleases
includingadditionalfeaturesandfunctionalities.
AlthoughNRdoesnothavetobebackward
compatiblewithLTE,thefutureevolutionofNR
shouldbebackwardcompatiblewithitsinitial
release(s).SinceNRmustsupportawiderange
ofusecases–manyofwhicharenotyetdefined–
forwardcompatibilityisofutmostimportance.
NRphysicallayerdesign
Aphysicallayerformsthebackboneofanywireless
technology.TheNRphysicallayerhasaflexible
andscalabledesigntosupportdiverseusecases
withextreme(andsometimescontradictory)
requirements,aswellasawiderangeoffrequencies
anddeploymentoptions.
THE NR PHYSICAL
LAYER HAS A FLEXIBLE
AND SCALABLE DESIGN
TO SUPPORT DIVERSE
USE CASES
BPSK–binaryphaseshiftkeying|BS–basestation|CDM–codedivisionmultiplexing|CPE–commonphaseerror|
CP-OFDM–cyclicprefixorthogonalfrequencydivisionmultiplexing|CSI-RS–channel-stateinformationreference
signal|D2D–device-to-device|DFT-SOFDM –discreteFouriertransformspreadorthogonalfrequencydivision
multiplexing|DL–downlink|DMRS–demodulationreferencesignal|eMBB–enhancedmobilebroadband|
eMBMS–evolvedmultimediabroadcastmulticastservice|FDM–frequencydivisionmultiplexing|HARQ–hybrid
automaticrepeatrequest|IMT-2020–thenextgenerationmobilecommunicationsystemstobespecifiedbyITU-R|
IoT–InternetofThings|ITU-R–InternationalTelecommunicationUnionRadiocommunicationSector|LBT–listen-
before-talk|LDPC–low-densityparity-check|MBB–mobilebroadband|MIMO–multiple-input,multiple-output|
mMTC–massivemachine-typecommunications|mmWave–millimeterwave|MU-MIMO–multi-userMIMO|
NR–NewRadio|PRB–physicalresourceblock|PTRS–phase-trackingreferencesignal|QAM–quadratureamplitude
modulation|QPSK–quadraturephaseshiftkeying|SRS–soundingreferencesignal|TDM–timedivisionmultiplexing
|UE–userequipment|UL–uplink|URLLC–ultra-reliablelow-latencycommunications
ALI A. ZAIDI,
ROBERT BALDEMAIR,
MATTIAS ANDERSSON,
SEBASTIAN FAXÉR,
VICENT MOLÉS-CASES,
ZHAO WANG
Far more than an evolution of mobile broad
band, 5G wireless access will be a key IoT
enabler, empowering people and industries
to achieve new heights in terms of efficiency
and innovation. A recent survey performed
across eight different industries (automotive,
finance, utilities, public safety, health care,
media, internet and manufacturing) revealed
that 89 percent of respondents expect 5G to
be a game changer in their industry [1].
■ 5Gwirelessaccessisbeingdevelopedwith
threebroadusecasefamiliesinmind:enhanced
mobilebroadband(eMBB),massivemachine-type
communications(mMTC)andultra-reliablelow-
latencycommunications(URLLC)[2,3].eMBB
focusesonacross-the-boardenhancements
tothedatarate,latency,userdensity,capacity
andcoverageofmobilebroadbandaccess.
mMTCisdesignedtoenablecommunication
betweendevicesthatarelow-cost,massivein
numberandbattery-driven,intendedtosupport
applicationssuchassmartmetering,logistics,
andfieldandbodysensors.Finally,URLLC
willmakeitpossiblefordevicesandmachines
tocommunicatewithultra-reliability,verylow
latencyandhighavailability,makingitidealfor
vehicularcommunication,industrialcontrol,
factoryautomation,remotesurgery,smartgrids
andpublicsafetyapplications.
Tomeetthecomplexandsometimescontradictory
requirementsofthesediverseusecases,5Gwill
encompassbothanevolutionoftoday’s4G(LTE)
Future networks will have to provide broadband access wherever needed
and support a diverse range of services including everything from robotic
surgery to virtual reality classrooms and self-driving cars. 5G New Radio is
designed to fit these requirements, with physical layer components that are
flexible, ultra-lean and forward-compatible.
THE 5G NR PHYSICAL LAYER
Designing
forthefuture

ThekeytechnologycomponentsoftheNR
physicallayeraremodulationschemes,waveform,
framestructure,referencesignals,multi-antenna
transmissionandchannelcoding.
Modulationschemes
LTEsupportstheQPSK,16QAM,64QAM
and256QAMmodulationformats,andallof
thesewillalsobesupportedbyNR.Inaddition,
3GPPhasincluded л/2-BPSKinULtoenable
afurtherreducedpeak-to-averagepowerratio
andenhancedpower-amplifierefficiencyat
lowerdatarates,whichisimportantformMTC
services,forexample.SinceNRwillcovera
widerangeofusecases,itislikelythatthesetof
supportedmodulationschemesmayexpand.
Forexample,1024QAMmaybecomepartof
theNRspecification,sincefixedpoint-to-point
backhaulalreadyusesmodulationordershigher
than256QAM.Differentmodulationschemesfor
differentUEcategoriesmayalsobeincludedinthe
NRspecification.
Waveform
3GPPhasagreedtoadoptCP-OFDMwitha
scalablenumerology(subcarrierspacing,cyclic
prefix)inbothULandDLuptoatleast52.6GHz.
Havingthesamewaveforminbothdirections
simplifiestheoveralldesign,especiallywith
respecttowirelessbackhaulinganddevice-to-
device(D2D)communications.Additionally,
thereissupportforDFT-SpreadOFDMin
ULforcoverage-limitedscenarios,withsingle
streamtransmissions(thatis,withoutspatial
multiplexing).Anyoperationthatistransparentto
areceivercanbeappliedontopofCP-OFDMat
thetransmitterside,suchaswindowing/filteringto
improvespectrumconfinement.
A scalable OFDM numerology is required
to enable diverse services on a wide range of
frequencies and deployments. The subcarrier
spacing is scalable according to 15×2n
kHz, where
n is an integer and 15kHz is the subcarrier spacing
used in LTE. The scaling factor 2n
ensures that
slots and symbols of different numerologies are
aligned in the time domain, which is important to
efficiently enable TDD networks [4]. The details
related to NR OFDM numerologies are shown
in Figure 2. The choice of parameter n depends
on various factors including type of deployment,
carrier frequency, service requirements
(latency, reliability and throughput), hardware
impairments (oscillator phase noise), mobility
and implementation complexity [5]. For example,
wider subcarrier spacing can be promising
for latency-critical services (URLLC), small
coverage areas and higher carrier frequencies.
Narrower subcarrier spacing can be utilized for
lower carrier frequencies, large coverage areas,
narrowband devices and evolved multimedia
broadcast multicast services (eMBMSs). It may
also be possible to support multiple services
simultaneously with different requirements on
the same carrier by multiplexing two different
numerologies (wider subcarrier spacing for
URLLC and lower subcarrier spacing for MBB/
mMTC/eMBMS, for example).
The spectrum of OFDM signal decays rather
slowly outside the transmission bandwidth. In
order to limit out-of-band emission, the spectrum
utilization for LTE is 90 percent. That is, 100 of the
111 possible physical resource blocks (PRBs) are
utilizedina20MHzbandwidthallocation.ForNR,
it has been agreed that the spectrum utilization
will be greater than 90 percent. Windowing and
filtering operations are viable ways to confine
the OFDM signal in the frequency domain. It is
important to note that the relationship between
spectrum efficiency and spectrum confinement is
not linear, since spectrum confinement techniques
can induce self-interference.
HAVING THE SAME
WAVEFORM IN BOTH
DIRECTIONS SIMPLIFIES
THE OVERALL DESIGN
Figure 1 5G vision: use cases, spectrum and deployments
5G use cases
5G deployments
5G
Extreme throughput
Enhanced spectral efficiency
Extended coverage
High connection density
Energy efficiency
Low complexity
Extended coverage
Low latency
Ultra reliability
Location precision
8k
1GHz 3GHz 10GHz 30GHz 100GHz
Macro / micro sites
Micro / pico sites
Pico sites
eMBB
mMTCURLLC
5G use cases
5G deployments
5G
Extreme throughput
Enhanced spectral efficiency
Extended coverage
High connection density
Energy efficiency
Low complexity
Extended coverage
Low latency
Ultra reliability
Location precision
8k
1GHz 3GHz 10GHz 30GHz 100GHz
Macro / micro sites
Micro / pico sites
Pico sites
eMBB
mMTCURLLC

Figure 2 Scalable OFDM numerology for NR Figure 3 TDD-based frame structure examples for eMBB, URLLC and operation in unlicensed spectrum using listen-before-talk (LBT)
Subcarrier spacing 15kHz
30kHz
(2 x 15kHz)
60kHz
(4 x 15kHz)
15 x 2n
kHz,
(n = 3, 4, ...)
OFDM symbol
duration
66.67 µs 33.33 µs 16.67 µs 66.67/2n
µs
Cyclic prefix
duration
4.69 µs 2.34 µs 1.17 µs 4.69/2n
µs
OFDM symbol
including CP
71.35 µs 35.68 µs 17.84 µs 71.35/2n
µs
Number of OFDM
symbols per slot
7 or 14 7 or 14 7 or 14 14
Slot duration
500 µs or
1,000 µs
250 µs or
500 µs
125 µs or
250 µs
1,000/2n
µs
Slot aggregation for DL-heavy transmission
(for example, for eMBB)
ULDLDLDLDLDLDLDLDLDLDLDLDLDLDLDLDLDLDLDLDLDLDLDLDLDLDL
slot slot
Slot aggregation for UL-heavy transmission
(for example, for eMBB)
DL ULULULULULULULULULULULULULULULULULULULULULULULULULUL
slot slot
Utilizing mini-slots for URLLC transmission
slot
variable length
variable start
ULDLDL
DL-heavy transmission with UL part UL
slot
DLDLDLDLDLDLDLDLDLDLDLDL
DL-only transmission with late start due
to LBT or relaxed base station
synchronization requirements slot
DLDLDLDLDLDLDLDLDLDLDLDLDL
UL-heavy transmission with DL control UL UL UL UL UL UL UL UL UL UL UL UL
slot
DL

signal(PTRS),soundingreferencesignal(SRS)and
channel-stateinformationreferencesignal(CSI-RS).
DMRSisusedtoestimatetheradiochannel
fordemodulation.DMRSisUE-specific,canbe
beamformed,confinedinascheduledresource,
andtransmittedonlywhennecessary,bothin
DLandUL.Tosupportmultiple-layerMIMO
transmission,multipleorthogonalDMRSports
canbescheduled,oneforeachlayer.Orthogonality
isachievedbyFDM(combstructure)andTDM
andCDM(withcyclicshiftofthebasesequenceor
orthogonalcovercodes).ThebasicDMRSpattern
isfrontloaded,astheDMRSdesigntakesinto
accounttheearlydecodingrequirementtosupport
low-latencyapplications.Forlow-speedscenarios,
DMRSuseslowdensityinthetimedomain.
However,forhigh-speedscenarios,thetime
densityofDMRSisincreasedtotrackfastchanges
intheradiochannel.
PTRSisintroducedinNRtoenable
compensationofoscillatorphasenoise.Typically,
phasenoiseincreasesasafunctionofoscillator
carrierfrequency.PTRScanthereforebeutilized
athighcarrierfrequencies(suchasmmWave)to
mitigatephasenoise.Oneofthemaindegradations
causedbyphasenoiseinanOFDMsignalisan
identicalphaserotationofallthesubcarriers,
knownascommonphaseerror(CPE).PTRSis
designedsothatithaslowdensityinthefrequency
domainandhighdensityinthetimedomain,since
thephaserotationproducedbyCPEisidentical
forallsubcarrierswithinanOFDMsymbol,but
thereislowcorrelationofphasenoiseacross
OFDMsymbols.PTRSisUE-specific,confined
inascheduledresourceandcanbebeamformed.
ThenumberofPTRSportscanbelowerthanthe
totalnumberofports,andorthogonalitybetween
PTRSportsisachievedbymeansofFDM.PTRS
isconfigurabledependingonthequalityofthe
oscillators,carrierfrequency,OFDMsubcarrier
spacing,andmodulationandcodingschemesused
fortransmission.
TheSRSistransmittedinULtoperform
CSImeasurementsmainlyforschedulingand
linkadaptation.ForNR,itisexpectedthatthe
SRSwillalsobeutilizedforreciprocity-based
precoderdesignformassiveMIMOandULbeam
management.ItislikelythattheSRSwillhavea
modularandflexibledesigntosupportdifferent
proceduresandUEcapabilities.Theapproachfor
CSI-RSissimilar.
Multi-antennatransmissions
NR will employ different antenna solutions
and techniques depending on which part of the
spectrum is used for its operation. For lower
frequencies, a low to moderate number of active
antennas (up to around 32 transmitter chains) is
assumed and FDD operation is common. In this
case, the acquisition of CSI requires transmission
of CSI-RS in the DL and CSI reporting in the
UL. The limited bandwidths available in this
frequency region require high spectral efficiency
enabled by multi-user MIMO (MU-MIMO)
and higher order spatial multiplexing, which is
achieved via higher resolution CSI reporting
compared with LTE.
Forhigherfrequencies,alargernumberof
antennascanbeemployedinagivenaperture,
whichincreasesthecapabilityforbeamforming
andMU-MIMO.Here,thespectrumallocations
areofTDDtypeandreciprocity-basedoperation
isassumed.Inthiscase,high-resolutionCSIinthe
formofexplicitchannelestimationsisacquired
byULchannelsounding.Suchhigh-resolution
CSIenablessophisticatedprecodingalgorithms
tobeemployedattheBS.Thismakesitpossibleto
increasemulti-userinterferencesuppression,for
example,butmightrequireadditionalUEfeedback
ofinter-cellinterferenceorcalibrationinformation
ifperfectreciprocitycannotbeassumed.
NR HAS AN ULTRA-
LEAN DESIGN THAT
MINIMIZES ALWAYS-ON
TRANSMISSIONS
Framestructure
NRframestructuresupportsTDDandFDD
transmissionsandoperationinbothlicensed
andunlicensedspectrum.Itenablesverylow
latency,fastHARQacknowledgements,dynamic
TDD,coexistencewithLTEandtransmissions
ofvariablelength(forexample,shortduration
forURLLCandlongdurationforeMBB).The
framestructurefollowsthreekeydesignprinciples
toenhanceforwardcompatibilityandreduce
interactionsbetweendifferentfeatures.
Thefirstprincipleisthattransmissionsareself-
contained.Datainaslotandinabeamisdecodable
onitsownwithoutdependencyonotherslotsand
beams.Thisimpliesthatreferencesignalsrequired
fordemodulationofdataareincludedinagivenslot
andagivenbeam.
Thesecondprincipleisthattransmissionsare
wellconfinedintimeandfrequency.Keeping
transmissionstogethermakesiteasiertointroduce
newtypesoftransmissionsinparallelwithlegacy
transmissionsinthefuture.NRframestructure
avoidsthemappingofcontrolchannelsacrossfull
systembandwidth.
Thethirdprincipleistoavoidstaticand/or
stricttimingrelationsacrossslotsandacross
differenttransmissiondirections.Forexample,
asynchronousHARQisusedinsteadofpredefined
retransmissiontime.
AsshowninFigure2,aslotinNRcomprisesseven
or14OFDMsymbolsfor≤60kHznumerologiesand
14OFDMsymbolsfor≥120kHznumerologies.Aslot
durationalsoscaleswiththechosennumerologysince
theOFDMsymboldurationisinverselyproportional
toitssubcarrierspacing.Figure3providesexamples
forTDD,withguardperiodsforUL/DLswitching.
Aslotcanbecomplementedbymini-slots
tosupporttransmissionswithaflexiblestart
positionandadurationshorterthanaregularslot
duration.Amini-slotcanbeasshortasoneOFDM
symbolandcanstartatanytime.Mini-slotscan
beusefulinvariousscenarios,includinglow-
latencytransmissions,transmissionsinunlicensed
spectrumandtransmissionsinthemillimeterwave
spectrum(mmWaveband).
Inlow-latencyscenarios,transmissionneeds
tobeginimmediatelywithoutwaitingforthestart
ofaslotboundary(URLLC,forexample).When
transmittinginunlicensedspectrum,itisbeneficial
tostarttransmissionimmediatelyafterLBT.When
transmittinginmmWaveband,thelargeamount
ofbandwidthavailableimpliesthatthepayload
supportedbyafewOFDMsymbolsislargeenough
formanyofthepackets.Figure3providesexamples
ofURLLC-andLBT-basedtransmissionin
unlicensedspectrumviamini-slotsandillustrates
thatmultipleslotscanbeaggregatedforservices
thatdonotrequireextremelylowlatency(eMBB,
forexample).Havingalongertransmissionduration
helpstoincreasecoverageorreducetheoverhead
duetoswitching(inTDD),transmissionofreference
signalsandcontrolinformation.
Thesameframestructurecanbeusedfor
FDD,byenablingsimultaneousreceptionand
transmission(thatis,DLandULcanoverlapin
time).Thisframestructureisalsoapplicableto
D2Dcommunications.Inthatcase,theDLslot
structurecanbeusedbythedevicethatisinitiating
(orscheduling)thetransmission,andtheULslot
structurecanbeusedbythedevicerespondingto
thetransmission.
NRframestructurealsoallowsforrapid
HARQacknowledgement,inwhichdecodingis
performedduringthereceptionofDLdataandthe
HARQacknowledgementispreparedbytheUE
duringtheguardperiod,whenswitchingfromDL
receptiontoULtransmission.
Toobtainlowlatency,aslot(orasetofslots
incaseofslotaggregation)isfront-loadedwith
controlsignalsandreferencesignalsatthe
beginningoftheslot(orsetofslots).
Referencesignals
NRhasanultra-leandesignthatminimizesalways-
ontransmissionstoenhancenetworkenergy
efficiencyandensureforwardcompatibility.In
contrasttothesetupinLTE,thereferencesignals
inNRaretransmittedonlywhennecessary.The
fourmainreferencesignalsarethedemodulation
referencesignal(DMRS),phase-trackingreference

Forevenhigherfrequencies(inthemmWave
range)ananalogbeamformingimplementation
istypicallyrequiredcurrently,whichlimitsthe
transmissiontoasinglebeamdirectionpertime
unitandradiochain.Sinceanisotropicantenna
elementisverysmallinthisfrequencyregionowing
totheshortcarrierwavelength,agreatnumber
ofantennaelementsisrequiredtomaintain
coverage.Beamformingneedstobeappliedat
boththetransmitterandreceiverendstocombat
theincreasedpathloss,evenforcontrolchannel
transmission.Anewtypeofbeammanagement
processforCSIacquisitionisrequired,inwhich
theBSneedstosweepradiotransmitterbeam
candidatessequentiallyintime,andtheUEneeds
tomaintainaproperradioreceiverbeamtoenable
receptionoftheselectedtransmitterbeam.
Tosupportthesediverseusecases,NRfeatures
ahighlyflexiblebutunifiedCSIframework,in
whichthereisreducedcouplingbetweenCSI
measurement,CSIreportingandtheactualDL
transmissioninNRcomparedwithLTE.The
CSIframeworkcanbeseenasatoolbox,where
differentCSIreportingsettingsandCSI-RS
resourcesettingsforchannelandinterference
measurementscanbemixedandmatchedso
theycorrespondtotheantennadeploymentand
transmissionschemeinuse,andwhereCSIreports
ondifferentbeamscanbedynamicallytriggered.
Theframeworkalsosupportsmoreadvanced
schemessuchasmulti-pointtransmissionand
coordination.Thecontrolanddatatransmissions,
inturn,followtheself-containedprinciple,where
allinformationrequiredtodecodethetransmission
(suchasaccompanyingDMRS)iscontainedwithin
thetransmissionitself.Asaresult,thenetworkcan
seamlesslychangethetransmissionpointorbeam
astheUEmovesinthenetwork.
Channelcoding
NRemployslow-densityparity-check(LDPC)
codesforthedatachannelandpolarcodesfor
thecontrolchannel.LDPCcodesaredefined
bytheirparity-checkmatrices,witheach
columnrepresentingacodedbit,andeachrow
representingaparity-checkequation.LDPCcodes
aredecodedbyexchangingmessagesbetween
variablesandparitychecksinaniterativemanner.
TheLDPCcodesproposedforNRuseaquasi-
cyclicstructure,wheretheparity-checkmatrixis
definedbyasmallerbasematrix.Eachentryofthe
basematrixrepresentseitheraZxZzeromatrixor
ashiftedZxZidentitymatrix.
UnliketheLDPCcodesimplementedinother
wirelesstechnologies,theLDPCcodesconsidered
forNRusearate-compatiblestructure,asshown
in Figure4.Thelightbluepart(topleft)ofthe
basematrixdefinesahighratecode,atarate
ofeither2/3or8/9.Additionalparitybitscan
begeneratedbyextendingthebasematrixand
includingtherowsandcolumnsmarkedindark
blue(bottomleft).Thisallowsfortransmissionat
lowercoderates,orforgenerationofadditional
paritybitssuchasthoseusedforHARQoperation
usingincrementalredundancysimilartoLTE.
Sincetheparity-checkmatrixforhighercode
ratesissmaller,decodinglatencyandcomplexity
decreasesforhighcoderates.Alongwiththehigh
degreeofparallelismachievablethroughthequasi-
cyclicstructure,thisallowsforveryhighpeak
throughputsandlowlatencies.Further,theparity-
checkmatrixcanbeextendedtolowerratesthan
theLTEturbocodes,whichrelyonrepetitionfor
coderatesbelow1/3.ThisallowstheLDPCcodes
toachievehighercodinggainsalsoatlowcoding
rates,makingthemsuitableforusecasesrequiring
highreliability.
Polarcodeswillbeusedforlayer1andlayer2
controlsignaling,exceptforveryshortmessages.
THE FRAMEWORK ALSO
SUPPORTS MORE ADVANCED
SCHEMES SUCH AS MULTI-
POINT TRANSMISSION AND
COORDINATION
Figure 4 Structure of NR LDPC matrices
Systematic bits Parity bits Additional parity bits

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References:
Polarcodesarearelativelyrecentinvention,
introducedbyArıkanin2008[6].Theyarethe
firstclassofcodesshowntoachievetheShannon
capacitywithreasonabledecodingcomplexityfor
awidevarietyofchannels.
Byconcatenatingthepolarcodewithanouter
codeandkeepingtrackofthemostlikelyvaluesof
previouslydecodedbitsatthedecoder(thelist),
goodperformanceisachievedatshorterblock
lengths,likethosetypicallyusedforlayer1and
layer2controlsignaling.Byusingalargerlistsize,
errorcorrectionperformanceimproves,atthecost
ofhighercomplexityatthedecoder.
Conclusion
Flexibility,ultra-leandesignandforward
compatibilityarethepillarsonwhichallthe5GNR
physicallayertechnologycomponents(modulation
schemes,waveform,framestructure,reference
signals,multi-antennatransmissionandchannel
coding) arebeingdesignedandbuilt.Thehighlevel
offlexibilityandscalabilityin5GNRwillenableitto
meettherequirementsofdiverseusecases,including
awiderangeofcarrierfrequenciesanddeployment
options.Itsbuilt-inforwardcompatibilitywill
ensurethat5GNRcaneasilyevolvetosupportany
unforeseenrequirements.
Ali A. Zaidi
◆ joined Ericsson in 2014,
where he is currently a
senior researcher working
with concept development
and standardization of radio
access technologies (NR
and LTE-Advanced Pro).
His research focuses on
mmWave communications,
indoor positioning, device-
to-device communications,
and systems for intelligent
transportation and
networked control. He
holds an M.Sc. and a Ph.D.
in telecommunications
from KTH Royal Institute of
Technology in Stockholm,
Sweden. Zaidi is currently
serving as a member of the
Technology Intelligence
Group Radio and a member
of the Young Advisory Board
at Ericsson Research.
Robert Baldemair
◆ is a master researcher
who joined Ericsson in 2000.
He spent several years
working with research and
development of radio access
technologies for LTE before
shifting his focus to wireless
access for 5G. He holds a
Dipl. Ing. and a Ph.D. from
the Technische Universität
Wien in Vienna, Austria.
He received the Ericsson
Inventor of the Year award
in 2010, and in 2014 he and
a group of colleagues were
nominated for the European
Inventor Award.
Mattias Andersson
◆ is an experienced
researcher who joined
Ericsson in 2014. His
work focuses on concept
development and
standardization of low-
latency communications,
carrier aggregation and
channel coding for LTE and
NR. He holds both an M.Sc.
in engineering physics and a
Ph.D. in telecommunications
Sweden.
Sebastian Faxér
◆ is a researcher at Ericsson
Research. He received an
M.Sc. in applied physics
and electrical engineering
from Linköping University,
Sweden, in 2014 and joined
Ericsson the same year.
Since then he has worked on
concept development and
standardization of multi-
antenna technologies for LTE
and NR.
Vicent Molés-Cases
Research. He received an
M.Sc. in telecommunication
engineering from the
Polytechnic University of
theauthors
Valencia in Spain in 2016
and joined Ericsson the
same year. Since then he
has worked on concept
development and 3GPP
standardization of reference
signals for NR.
Zhao Wang
Research who joined the
company in 2015. He holds a
Ph.D. in telecommunications
Sweden. His work currently
focuses on reference signal
design of NR for 3GPP
standardizations.

✱ 5G NETWORK PROGRAMMABILITY 5G NETWORK PROGRAMMABILITY ✱
Driversofnetworkprogrammability
Thekeydriversbehindthecreationofa
programmablenetworkaretheneedtoaccelerate
timetomarket,andthedesiretoreduceoperational
costsandtakeadvantageofthebusiness
opportunitiespresentedbyanewmission-critical
servicemarket.Inaprogrammablenetwork,
traditionalnetworkfunctionsrequiringspecialized
hardwarearereplacedwithsoftwarefunctions
hostedoncommercialoff-the-shelfinfrastructure.
Technologiessuchassoftware-definednetworking
andnetworkfunctionsvirtualizationareessential
tocuttingoperationalandcapitalcostsinmobile
networks.
Cloud-basedservicesandapplicationsare
enablersforprogrammability.Serviceprovisioning
inthecloudandmanagedaccesstotheprovisioned
servicesandapplicationsareimportant.This
requirescollaborationbetweentelecomandother
industries(ITapplicationandcontentproviders,
andautomotiveoriginalequipmentmanufacturers,
forexample).Onewaytosimplifyandacceleratethe
deploymentofservicesandapplicationsfrom
industryverticalsistheautomatictranslationof
industrialrequirementstoservicerequirements,
andthenontoresource-levelrequirements(in
otherwords,networkrequirements).Network
slicingprovidesadedicated,virtualizedmobile
networkcontainingasetofnetworkresources,and
providesguaranteedQoS.Thenetworkslicesare
notonlybeneficialbutalsocriticaltosupportmany
applicationsinverticalindustries.
Newnetworkcommunicationservicescanalso
beprovisionedprogrammatically;thatis,
byusingasoftwareserviceorchestrationfunction
insteadofmanualprovisioningbyengineers.
Asorchestrationwillalsobeusedforprovisioning
connectivityservicestomission-critical
applications,mobilenetworksneedtosupport
QoSprogrammability.
Mission-criticalIntelligent
TransportationSystemusecases
5Gwillsupportadiverserangeofusecases
indifferentindustrysectors,eachputtingitsown
QoSrequirementsonthemobilenetwork[1].Itis
possibletousenetworkprogrammabilitytorealize
mission-criticalusecaseswithQoSrequirements
bycreatinghighlyspecializedservicestailored
toindustrialneedsandpreferences.
Featureslikelowerlatency(reactiontimesthat
arefivetimesfaster),higherthroughput(10to100
timeshigherdatarate)andanenormousincreasein
thenumberofconnecteddevices(10to100times
more)cansupportthelarge-scaleuseofmassive
machine-typecommunication(mMTC)and
mission-criticalMTC(MC-MTC)usecasesforthe
firsttime.Further,adedicatednetworkslicewould
meetthespecificrequirementsofeachusecase.
InmMTCusecases,alargenumberofsensors
andactuatorsareconnectedusingashort-range
radio(capillarynetwork)toabasestation(eNodeB)
usingalowprotocoloverheadtosavethebattery
lifeofthedevices.
Thisrequiresanetworkslicewithbroadcoverage,
smalldatavolumesfrommassivenumbersof
devices.MC-MTCusecasesemphasizelower
latency(downtoalevelofmilliseconds),
A key differentiator of 5G systems from
previous generations will be a higher degree
of programmability. Instead of a one-size-fits-
all mobile broadband service, 5G will provide
the flexibility to tailor QoS to connectivity
services to meet the demands of enterprise
customers. This enables a new range of
mission-critical use cases, such as those
involving connected cars, manufacturing
robots, remote surgery equipment, precision
agriculture equipment, and so on.
■ Networkprogrammabilitycansupportrapid
deploymentofnewusecasesbycombining
cloud-basedserviceswithmobilenetwork
infrastructureandtakingadvantageofnewlevels
offlexibility.Further,networkprogrammabilitywill
enableagreaternumberofenterprisecustomersto
usesuchservices,andconsumerswillbenefitfrom
auniqueandpersonalizedexperience.
Anumberofusecasesinmission-critical
scenarioscanbenefitfromQoSprogrammability
becauseacellularnetwork’sconnectivity
requirements–includinglatency,throughput,
servicelifetimeandcost–varywidelyacross
differentusecases.Tosupportthemall,wehave
developedanapplicationprogramminginterface
(API)thatallowsthirdpartiestospecifyand
requestnetworkQoS.Wehavealsodemonstrated
theusefulnessofthisAPIonatestmobilenetwork
usingatransport-relatedusecase.
Aspartofthisusecase,wehavebeencollaborating
withcommercialvehiclemanufacturerScaniato
developtheQoSrequirementsforteleoperation.
Teleoperationistheremoteoperationofan
autonomousvehiclebyahumanoperatorincases
wherethevehicleencountersasituationthatthe
autonomoussystemcannotovercomebyitself(a
roadobstacleormalfunction,forexample).
RAFIA INAM,
ATHANASIOS
KARAPANTELAKIS,
LEONID MOKRUSHIN,
ELENA FERSMAN
5G will make it possible for mobile network operators to support
enterprises in a wide range of industry segments by providing cellular
connectivity to mission-critical applications. The ability to expose policy
control to enterprise verticals will create new business opportunities for
mobile network operators by enabling a new value chain through the
integration of telecom with other industries.
FOR MISSION-CRITICAL APPLICATIONS
5Gnetwork
programmability
AAR–AuthenticationAuthorizationRequest|AF–applicationfunction|API –applicationprogramminginterface
eNB–eNodeB|EPC–EvolvedPacketCore|EPS–EvolvedPacketSystem|HSS–HomeSubscriberServer|
ITS–IntelligentTransportationSystem|MME–MobilityManagementEntity|mMTC–massivemachine-type
communication|MTC–machine-typecommunication|PCRF–policyandchargingrulesfunction|PDN–packet
datanetwork|PGW–PDNgateway|QCI–QoSclassidentifier|Rx–radioreceiver|SAPC–Service-Aware
PolicyController|SGW–servicegateway|UDP–UserDatagramProtocol |UE–userequipment

supportthevolumeanddiversityofusecases
withthecurrentnetworkmanagementapproach.
Theyneedadifferentmeansofmanagingthe
networktostaycompetitive.Inourview,
developinganAPIisthelogicalfirststeptoward
exposingaprogrammablenetworktotheindustry
verticals.Thisapproachwillresultinasolutionthat
ismoreresponsivethanrigidcommercialofferings,
suchaspreconfiguredsubscriptionpackages.
ArchitectureoftheEricsson-Scaniaproject
Teleoperatingabusrequiresdatafromsensorson
thebus,includingavideofeedfromacameraatthe
frontthatisstreamedtoaremoteoperationscenter
overLTEradioaccesswithanevolved5Gcore
network.Thecommandstodrivethebusaresent
fromthecentertothebususingScania’scommand
system.
Figure1illustratesthedatastreamsthatneedto
beprioritizedtomeetQoSdemands:sensordata,
thevideofeedoriginatingfromthevehicleuser
equipment(UE)andthecommandstoremotely
drivethebus.Sendingthesedatastreamsover
low-prioritydatatraffic(likeinfotainment)
isacriticalrequirement.WeusedQOSclass
identifier(QCI)bearers,asdetailedinthe
corresponding3GPPstandard,toenforcethis
prioritization.WeassignedQCIclass5and2to
videoandsensordatarespectivelyandlowest-
priorityQCIclass9toinfotainment.Inourlab
environment,wehaveconfirmedthatthehigh-
prioritystreams(QCI2and5)canbekept
regardlessoftheamountoflow-priority
backgrounddatatrafficinthenetwork[3,4].
Thenextstepwillbetotestourtestbedsetup
fortheprioritizedvideostreaminthepresence
ofthenetworkloadduetoinfotainment-type
backgroundtraffic.
Howitworks
Acloud-hostedapplicationfunction(AF)
dynamicallysetsupvirtualconnectionsbetween
vehiclesandthe5GEvolvedPacketCore(EPC)
network,withspecificQoSattributes,suchas
designatedlatencylevelsandguaranteedthroughput.
Figure2illustratesthearchitectureofthesystem
onwhichwehaveimplementedtheAPI.Inaddition
todeployingastandardEPCandLTEband-40RAN,
anAFisdeployedonanOpenStack-managedcloud.
Thisapplicationfunctionalityallowsthirdparties
tosetupQoSfortheirUEsthroughanAPI.
TheAFconsistsofthefollowingcomponents:
aknowledgebasemodule,anAPIendpointmodule
andatransformer.
Knowledgebasemodule
Thismodulemapsdomain-specificconcepts
tothegenericconcepts.Theknowledgebaseis
implementedasagraphdatabase,hasaschema
ofgeneralconceptsandcanbeextendedwith
additionaldomainconceptdocumentsthat
instantiatethegeneralconceptschema.Theschema
includesabasicvocabularyofgeneralconceptsthat
modelQoSrequests.Theseconceptscanbe
instantiatedindomainconceptsforaspecific
enterprise.Inourcase,theenterprise
isautomotive.
Withintheknowledgebasemodule,an“agent”
isastringthatissemanticallyrelatedtothemobile
deviceforwhichQoSisrequested.Inourcase,the
agentisinstantiatedwiththe“vehicle”domain
concept.QoSclassidentifiers(QCIs)areindicators
ofnetworkQoSforagivenagent.TheQCIconcept
wasintroducedin3GPPTS23.203Release8,
withadditionalclassesbeingintroducedin
Release12andRelease14.
EveryQCIclasshasanintegeridentifier,for
exampleQCI1orQCI2,andismappedtoasetof
QoSmetricssuchasanindicatorofpriorityofdata
traffic,anupperceilingfornetworklatencyand,in
somecases,guaranteedbitrate.Inourcase,
QCIsareinstantiatedwithdomainconceptsfor
real-timevehicletrafficdomainconcepts.
Forexample,QCI3isinstantiatedas“vehicle_
control_traffic”andQCI4isinstantiatedas
“vehicle_video_traffic.”Forusersbrowsingtheir
mobiledevicesinthevehicles,weinstantiatea
low-priorityclassQCI9as vehicle_web_browsing.”
Datatrafficdescriptorsaregenericcontentsthat
canconfigureeachQCI.Theconfiguration
robusttransmissionandmultileveldiversitydue
totheirmission-criticalnatureand,consequently,
needanetworksliceofverylowlatency,high
reliabilityandavailability(packetlossdownto
10-9).Thisispossiblebycreatingasliceofveryhigh
priority.Weenvisionrealizingtheseusecases
withaflexiblenetworkprogrammabilitytechnique.
Thecurrentfocusofourresearchiswithinthe
IntelligentTransportationSystem(ITS)domain
andincludesafew5GusecasesinmMTCand
MC-MTC,includingtransportationandlogistics,
autonomouscarsandteleoperatedvehicles.
Transportationandlogistics
Thelowerlatencyandhighthroughputof5G
willsupportmultipleusecasesrelatedtoconnected
cars,transportationandretaillogisticsthat
consistoffleetsofconnected/driverlessvehicles
transportingpeopleandgoods.Thekeynetwork
requirementsformission-criticalautomotive
drivingarehighthroughputandlowlatencyupto
100ms.Failureisnotanoptioninthesecases.
Therearealsomanypotentialsub-usecases.
Forexample,ajourneyfromAtoBinadriverless
vehiclecouldinvolvevehicle-to-vehicleconnections,
connectionsbetweenvehiclesandstreet
infrastructurefortrafficmanagement,and
high-speedreliableconnectivitytosupportcloud
applications.
Autonomousvehicles
Thevisionoffullyautonomousvehiclesaimsto
reducetherisksassociatedwithhumanerror.
Asystemtoachievethisvisionwouldneedto
connectthecarsandtheroadinfrastructurewith
1mslatencyinallareas(100percentcoverage).
Unfortunately,1mslatencyiscurrentlynotpossible
inmobilenetworks.However,thebandwidth
requirementstomakethispossiblearenot
excessive,asonlyvehiclecontroldataneedstobe
communicated.Thiscapabilityisexpectedin5G.
Teleoperationofvehicles
Theabilitytocontrolaself-drivingvehiclefroma
distanceisanimportantusecasethatisneededin
publictransportationwhenanonboard,autonomous
systemfacesadifficultsituation,suchasatraffic
accident,anunexpecteddemonstration,unscheduled
roadworksorflooding.Thesescenariosrequirethe
planningofanalternateroute,andanoperator
needstodrivethevehicleremotelyforashorttime.
Anothercasecouldbeamechanicalmalfunctionor
aninjuryonabusthatrequiresremoteintervention
tomitigatetheriskofdangertoothers.Network
requirementsforremotemonitoringandcontrol
includebroadcoverage,highdatathroughputand
lowlatencytoenablecontinuousvideostreaming
andtheabilitytosendcommandsbetweenaremote
operationscenterandavehicle[2].
WhyanAPI?
ToguaranteeQoSforthethreeITScasesdescribed
above(andmission-criticalusecasesingeneral)
mobilenetworkoperatorstypicallygothrough
manualnetworkplanningandconfigurations.
Examplesincludeconfiguringmanualdataroutes
viadifferentrouters,configuringDifferentiated
Servicesandallocatingdedicatedspectrumranges
toeachusecase.However,doingthisiscostlybecause
itrequirestheconfigurationanddeploymentof
networkequipment.Norisitparticularlyfeasible,
asthiskindofconfigurationdeploymentcannotbe
donemerelyinpartsofthetransportnetwork(such
asbackhaul).If,ontheotherhand,theresources
werevirtualizedandtherewassoftwarethatcould
setuptheseroutesoverthesamephysicalnetwork
link,bothofthelimitingfactorswouldbeeliminated:
thecostofconfigurationandthedeploymentof
multipleroutes.Asaresult,itwouldbeboth
financiallyandtechnicallyfeasibletosupport
theseusecasesconcurrently.
Itisclearthatoperatorswillnotbeableto
WEENVISIONREALIZING
THESEUSECASESWITHA
FLEXIBLENETWORK
PROGRAMMABILITY
TECHNIQUE

pertainstoacharacterizationofthetrafficinterms
ofrequiredthroughputforboth“uplink”and
“downlink,”theformerbeingdatatraffic
transmittedfromtheagentandthelatterbeingthe
opposite.Optionally,descriptorsmayalsocontain
thetypeofdatapacketsexchanged(forexample,
UDP/IPorTCP/IP),aswellaspotentiallytheport
orportrange.Forexample,inthecaseof
“vehicle_control_traffic,”thedatatraffic
descriptoridentifiesanuplinkbandwidth
of1Mbpsandadownlinkbandwidthof1Kbps.
Acombinationofagents,QCIsandtheir
associateddatatrafficdescriptorsarestored
intheknowledgebaseasadomainconcept
document.Everyusecasehasitsowndomain
conceptdocument,whileeachspecificenterprise
hasmorethanonedocument.Forexample,inour
case,thereisan“automotive/teleoperation”
document.However,otherdocumentsfor
automotivecanalsoexist,suchas“automotive/
autonomousdrive”or“automotive/remotefleet
management.”Becausethedataisstoredaslinked
data,conceptsfromone domainconceptdocument
canbereusedinanother.
APIendpointmodule
ThismodulecomposesAPIspecificationsfrom
everydomainconceptdocumentintheknowledge
base.ThisAPIspecificationisRESTful,uses
symmetricencryption(HTTPS)andcanbecalled
Figure 1 Prioritized data streams to meet QoS demands
(5G) core
network
Request to prioritize vehicle data traffic
Data traffic
prioritization interface
Scania bus
Ericsson test
network at Scania
test track Control plane
User plane Prioritized video traffic
Prioritized control traffic
Non-prioritized traffic
(e.g. mobile broadband, infotainment)
User plane
User plane
Scania
command
center Remote
driver
Ericsson Cloud
Network traffic prioritization
Internet
Figure 2 Architecture for programmable QoS in existing an LTE EPC network
Knowledge
base
API endpoint
Ericsson
research
cloud
Generic API
call
UEUE
eNB
Domain
knowledge
definition
Domain
semantics
Third party
AF
EPC
QoS setup
Uu Uu
Rx
S6a
S5/S8
S1-MME
S1-U
S1-MME
S1-U
UEUE
eNB
Uu Uu
HSS
SAPCPGW
SGW
MME
Transformer
Gx
fromanythirdparty.TheseAPIcallsgettranslated
intogenericconceptcallsthataresubsequently
senttothetransformermodule.Notethat,in
additiontoanAPIcallforsetupofspecializedQoS,
thereisanotherAPIcallforteardownofthisQoS.
Forexample,whenavehicleisdecommissioned
ordoesnotneedtobeteleoperated,therecanbe
acalltoteardowntheQoStunnel,sonetwork
resourcescanbeallocatedtoUEsinothervehicles
ordevices.Figure3providesanoverviewof
domain-specificandgenericrequests.
Transformermodule
Thetransformermoduletranslatesgenericrequests
forQoStoRxAARrequests,astheserequestsare
specifiedin3GPPTS29.214.TheRxrequestsare
sentdirectlytothePCRFnodeinordertosetup
the“EPSbearer”(inotherwords,thedatatunnel
withtherequestedQoS).Asisthecasewiththe
endpointmodule,thetransformermodulecanalso
translateateardownrequesttoanRxrequestto
reverttothelowest-prioritydefaultbearer
(inmostcases,QCI9).
CONCEPTSFROM
ONEDOMAINCONCEPT
DOCUMENTCANBEREUSED
INANOTHER

#01 2018 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ #01 2018 37
Figure 3 Overview of requests
Domain specific request Generic request Description of the request
GET /vehicle GET /agent Retrieve QoS information
for all UEs
GET /vehicle/<IP> GET /agent/<IP> Retrieve QoS information
for one UE, based on its IP
address
GET /qos GET /qos Retrieve QoS for all UEs
GET /qos/vehicle_control_traffic GET /qos/QCI3 Retrieve all UEs with QCI3
bearer setup
POST /qos/
{
“source_IP”:<src_IP>,
“source_port”:<src_port>,
“destination_IP”:<dst_IP>,
“destination_port”:<dst_port>,
“qos_class”:”vehicle_control_
traffic”,
“protocol”:”TCP”,
“type”:”vehicle_video_stream”
}
POST /qos
{
“destination_port”:<dst_port>
“qos_class”:”QCI3”,
“protocol”:”TCP”,
“max-requested-bandwidth-
UL”:”1024,
“max-requested-bandwidth-
DL”:”100”
}
Set QoS for UE with IP src_
IP and port src_port toward
destination with IP dst_IP
and port dst_port. Protocol
in this example is TCP but it
can also be UDP.
DELETE /vehicle
{
“protocol”:”TCP”
}
DELETE /agent
{
“protocol”:”TCP”
}
Remove QoS for UE
with given source and
destination IP and port
Testbedresults
ToassessQoS,weperformedexperimentsonthe
uplinkprioritizedvideostreamusingQCI5inthe
presenceofthenetworkloadduetoinfotainment-
typebackgroundtrafficusingQCI9.Thetotal
measuredavailablebandwidthonthenetwork
wasapproximately8.55Mbps.Wetestedseveral
networkloadscenariosandmeasuredtheresults
againstthreebackgroundtrafficconditions:
〉〉 none(0Mbps)
〉〉 some(4.2Mbpsor49percentoftheavailablebandwidth)
〉〉 extreme(8.55Mbpsor100percentoftheavailable
bandwidth)
Wemeasuredboththroughputandone-way
networkdelayunderthesetrafficconditions.
Wealsomeasuredtheratioofpacketslost
versuspacketssenttotestthethroughput
qualityofthenetworkforthreedifferent
qualitiesofvideostreams:
〉〉 excellent(6Mbpsor70percentoftheavailablebandwidth)
〉〉 good(3Mbpsor35percentoftheavailablebandwidth)
〉〉 borderlinedrivable(2Mbpsor23percentoftheavailable
bandwidth)
Borderlinedrivableistheminimumrequirementto
performteleoperation.Weobtainedthepacket
Figure 4 Packet loss (in percentage of total packets) in best effort (QCIŁ9) and prioritized (QCI5) bearers
100.000
10.000
1.000
0.100
0.010
0.001
No background traffic
(0 Mbps)
Some background traffic
(4.2Mbps)
QCI9 QCI5
No background traffic
(0 Mbps)
Some background traffic
(4.2Mbps)
Extreme background traffic
(8.55Mbps)
Extreme background traffic
(8.55Mbps)
2Mbps
video
3Mbps
video
6Mbps
video
2Mbps
video
3Mbps
video
6Mbps
video
2Mbps
video
3Mbps
video
6Mbps
video
2Mbps
video
3Mbps
video
6Mbps
video
2Mbps
video
3Mbps
video
6Mbps
video
2Mbps
video
3Mbps
video
6Mbps
video
0.049715 0.045638 0.042681
0.048628 0.045809 0.050838 0.049232 0.046540 0.046033
0.053639 0.051253 0.057156
0.044295
50.7720697 45.764087 47.333316
1.3418724
5.086253
Color interpretation
<= 0.08% packet loss: unnoticeable in video
>0.08 – 0.5%: ghosting effect
0.5% – 1%: artificial movement/dropped frames
1% – 5%: long pauses
5%+ impossible to follow
Percentageofpacketslost
36

Rafia Inam
◆ joined Ericsson Research
in 2015. She works as a
senior researcher in the
area of machine intelligence
and automation, and her
research interests include
5G management, service
modeling, virtualization
of resources, reusability
of real-time software and
ITSs. Inam received her M.S.
from Chalmers University of
Technology in Gothenburg,
Sweden, in 2010. She
received her Licentiate
and doctoral degrees from
Mälardalen University in
Västerås, Sweden in 2012
and 2014 respectively. Her
paper, “Towards automated
service-oriented lifecycle
management for 5G
networks”, won a best
paper award in 2015.
Athanasios
Karapantelakis
◆ joined Ericsson in
2007 and currently works
as a master research
engineer in the area of
machine intelligence and
automation. He holds a B.Sc.
in computer science from
the University of Crete in
Greece and an M.Sc. and
Licentiate of Engineering
in communication systems
Sweden. His background is
in software engineering.
Leonid Mokrushin
◆ is a senior specialist
in the area of cognitive
technologies. His current
focus is on investigating
what technological
opportunities artificial
intelligence may bring to
Ericsson by creating and
prototyping innovative
concepts in the context
of new industrial and
telco use cases. He joined
Ericsson Research in 2007
after postgraduate studies
at Uppsala University in
Sweden. He holds an M.Sc.
in software engineering
from Peter the Great St.
Petersburg Polytechnic
University.
Elena Fersman
◆ is head of machine
intelligence and automation
at Ericsson Research and
an adjunct professor in
cyber-physical systems
at KTH Royal Institute of
Technology in Stockholm.
She holds a Ph.D. in
computer science from
Uppsala University in
Sweden and did post-
doctoral research at École
normale supérieure Paris-
Saclay, France, before
starting her industrial
career. Her current research
interests are in the areas of
modeling and analysis and of
software- and knowledge-
intensive intelligent systems
applied to 5G and IoT.
theauthors
Further reading
〉〉 YouTube, Remote bus driving over 5G,
November 2016: https://www.youtube.com/
watch?v=lPyzGTD5FtM
〉〉 Ericsson Research blog, 5G teleoperated
vehicles for future public transport, June
8, 2017, Berggren, V; Fersman, E; Inam, R;
Karapantelakis, A; Mokrushin, L; Schrammar,
N; Vulgarakis, A; Wang, K: https://www.
ericsson.com/research-blog/5g/5g-teleoperated-
vehicles-future-public-transport/
〉〉 EricssonMobilityReport,June2017:
https://www.ericsson.com/assets/local/mobility-
report/documents/2017/ericsson-mobility-report-
june-2017.pdf
Theauthors
wouldliketo
acknowledge
theworkof
Keven(Qi)Wang
onthisproject
duringhisstayat
Ericsson.
droprequirementsfromempiricalobservations
duringtestdriving.Wetookatotalof160
measurementsforeachexperimentandplotted
thegraphsbasedontherespectiveaveragevalue.
Measurementsfromthe5G-networktestbed
showthatresourceprioritizationcanassure
predefinedQoSlevelsformission-critical
applications,regardlessofbackgroundtraffic.
Figure4illustratesguaranteeduplinkpacketloss
foracriticalapplication,inwhichtheacceptable
packetlossoflessthanorequalto0.08percentis
unnoticeableinthevideostream.Thisistrueeven
withextremebackgroundtrafficwhenthesystemis
congested–thecriticaltrafficisstillservedwithno
performancedegradation.
However,asFigure4alsoillustrates,forthe
non-prioritizedinfotainmenttraffic(QCI9)the
packetlossincreasesheavilywiththeincrease
inthebackgroundtraffic,introducinglongpauses
andmakingteleoperationimpossibleevenfora
lowerlevelofcongestion.
Whenwemeasuredtheuplinkdelay,wefound
thatitispreserved(remainingatlessthan34ms)
forthecriticalvideotrafficevenwhenthesystem
exhibitscongestion.Forthenon-prioritizedtraffic,
thedelayreachesupto600msduringcongestion.
Thenextstepistodeveloptheconceptfora
self-serviceportalwherenetworkcustomers
couldspecifyQoSrequirementsontheirownterms;
forexample,toprioritize4Kvideotrafficfor
40busesinanurbanscenario.Thesoftwarewould
thentranslatethisspecificationintoinstructions
fornetworkresourceprioritization.
References
1. IEEE,InternationalConferenceonIntelligentTransportationSystems(November2016)–Feasibility
AssessmenttoRealiseVehicleTeleoperationusingCellularNetworks–RafiaInam,NicolasSchrammar,Keven
Wang,AthanasiosKarapantelakis,LeonidMokrushin,AnetaVulgarakisFeljanandElenaFersman,availableat:
http://ieeexplore.ieee.org/document/7795920/
2. IEEE,InternationalConferenceonFutureInternetofThingsandCloud(August2016)–DevOpsforIoT
ApplicationsUsingCellularNetworksandCloud–AthanasiosKarapantelakis,HongxinLiang,KevenWang,
KonstantinosVandikas,RafiaInam,ElenaFersman,IgnacioMulas-Viela,NicolasSeyvetandVasileios
Giannokostas,availableat:http://ieeexplore.ieee.org/document/7575883/
3. EricssonMobilityReport,ImprovingPublicTransportwith5G,November2015,availableat:https://www.
ericsson.com/res/docs/2015/mobility-report/emr-nov-2015-improving-public-transport-with-5g.pdf
4. IEEE,ConferenceonEmergingTechnologiesandFactoryAutomation(September2015)–Towardsautomated
service-orientedlifecyclemanagementfor5Gnetworks(BestPaper)–RafiaInam,AthanasiosKarapantelakis,
KonstantinosVandikas,LeonidMokrushin,AnetaVulgarakisFeljan,andElenaFersman,availableat:http://
ieeexplore.ieee.org/document/7301660/
Conclusion
5Gattributessuchasnetworkslicingandlow
latencywillsoonmakemission-criticalusecases
suchassafe,autonomouspublictransportareality.
Automatednetworkresourceprioritizationviaa
programmableAPIcansupportnetworkQoSfor
diverseusecaseswithdifferentconnectivity
requirementsonthecellularnetwork.Bydeveloping
anAPIthatallowsathirdpartytorequestnetwork
resourcesandimplementingitonatestmobile
network,wehavedemonstratedhowthetechnology
worksinanurbantransport-relatedusecasewith
Scania.Theinitialresultsshowthatthroughputand
latencyaremaintainedforhigh-prioritystreams
regardlessofthenetworkload.

enabledby
implementingbreathtakinglyinnovativeconcepts
asaresult.Forexample,the2017Hannover
IndustryFairshowcasedseveralinnovationssuch
ascollaborativerobots,smartmaterials,adaptive
production,andself-learningsystems,whichwere
ontheirwayoutoflabsandontorealproduction
shopfloors.Germanindustryiscommittedto
makingIndustry4.0thenewbenchmarkin
productionefficiency,withplanstoinvestEUR40
billionannuallyuntil2020.
ThestrengthoftheIndustry4.0initiativeliesin
thefactthatithasbeenajointeffortofgovernment,
universitiesandindustriessincedayone.Together
theyfostergrowth,usingfewerresources,reducing
riskandboostingproductivityandflexibility.The
initiativehasdevelopedmanygroundbreaking
conceptsthathaveresultedinaquantumleapin
thenetworkingofhumans,machines,robotsand
products.Manufacturingleadersarecombining
informationtechnologyandoperationstechnology
tocreatevalueinentirelynewways.Thesecyber-
physicalproductionlinesarecutting-edgetoday,but
willbethestandardoftomorrow.Bytheendof2017,
Industry4.0hadgrownintoatrulyinternational
initiativewithcontributorsandusersallover
theglobe,changingthebasisofcompetitionin
productionautomationforgood.
Guaranteedreal-timecommunicationbetween
humans,robots,factorylogisticsandproducts
isafundamentalprerequisiteoftheIndustry4.0
concept.Real-timedatawillgeneratetransparency
andactionableinsights,whileedgeanalyticswill
helpreapmaximummachinevalueandoptimize
production.Alloftheaboveconceptsclearly
requirestandardizationand(data)security.With
itsstandardizednetworkingcapabilities,built-in
security,guaranteedgradesofservice,aswellas
distributedcloudandnetworkslicingconcepts,5G
isaperfecttoolforadvancedindustriesthatwantto
takeadvantageofdigitaltransformation.
IoT–InternetofThings|OEM–originalequipmentmanufacturer|PLC–programmablelogiccontroller|
PoC–proofofconcept
ROBERTO SABELLA
(ERICSSON),
ANDREAS THUELIG
(ERICSSON),
MARIA CHIARA
CARROZZA
(SANT’ANNA SCHOOL
OF ADVANCED
STUDIES),
MASSIMO IPPOLITO
(COMAU)
The combination of robotics, machine
intelligence and 5G networks will provide a
wealth of opportunities for cooperation
between robots and humans that can improve
productivity and speed up the delivery of
services for citizens.
■ Mostanalystsagreethatsmartmanufacturing
islikelytorepresentthebiggestportionofmarket
revenuesfortheInternetofThings(IoT)inthe
nearfuture.Smartmanufacturingisdependenton
industrialautomation,whichreliesheavilyonthe
useofrobotsandmachineintelligence.The
factoryofthefuturewillberealizedthroughthe
digitizationofthemanufacturingprocessand
plants,whichwillbeenabledby5Gnetworksand
alltheirbuildingblocks.Asaleaderin5G
infrastructure–includingcloudtechnologies,big
dataanalyticsandITcapabilities–Ericssonis
wellplacedtotakealeadingroleinthis
transformationandpartnerwithindustriesto
developsolutionsthataretailoredtofittheir
needs.OurfruitfulcollaborationwithComau,a
worldleaderinindustrialautomation,andthe
Sant’AnnaSchoolofAdvancedStudies,ahighly
regardedacademiccenterofexcellencein
robotics,isthefirststep.
UnderstandingIndustry4.0
TheGermangovernmentlaunchedtheIndustry
4.0initiativein2011tofosterthecompetitiveness
ofitsindustriesforthedecadestocome.Seven
yearslater,wecanseethatleadingindustriesare
The emergent “fourth industrial revolution” will have a profound impact on
both industry and society in the years ahead. Robotics, machine intelligence
and 5G networks in particular will play major roles in this revolution by enabling
ever higher levels of automation for production processes.
ROBOTICS, MACHINE INTELLIGENCE AND 5G
Industrial
automation
FEATURE ARTICLE ✱✱ FEATURE ARTICLE
40 ERICSSON TECHNOLOGY REVIEW ✱ #01 2018 #01 2018 ✱ ERICSSON TECHNOLOGY REVIEW 41

Figure1showsthedevelopmentpaththatallows
industriestoevolvestep-by-steptowardthefull
Industry4.0transformationprocess[1].Itconsists
ofsixstageswitheachbuildingontheprevious
one.Eachstageincludesadescriptionofthe
necessarycapabilitiesandtheconsequentbenefit
forcompanies.Connectivityisthesecondstage,
immediatelyaftercomputerization,anditenables
theonesthatfollow.
Thetransformationofmanufacturing
Thetransformationofthemanufacturingindustry
[2]frommassproductiontomasscustomization
throughdigitizedfactoryoperationsisillustrated
inFigure2.Althoughtheindustrialrevolutionat
theendofthe18thcenturyledtotheadventof
mechanization,industrialproductionremainedat
anartisanalleveluntilthe20thcentury,whentrue
massproductionbeganwithautomotive
manufacturing.Assembly-lineproduction
becameaparadigmformassproductionandhad
far-reachingimpactsonsociety.Infact,whatis
called“Fordism”insocialsciencedescribesan
economicandsocialsystembasedon
industrialized,standardizedmassproductionand
massconsumption.Thekeyconceptisthe
manufacturingofstandardizedproductsinhuge
volumes,usingspecial-purposemachineryand,at
thattime,unskilledlabor.AlthoughFordismwasa
methodusedtoimproveproductivityinthe
automotiveindustry,theprincipleappliedtoany
kindofmanufacturingprocess.
Inrecentdecades,therehasbeenanincreasing
needforcustomizationtoallowmanufacturersto
differentiatefromcompetitorsandbroadentheir
productofferings.Inaway,theproductvariety
stimulatestheconsumermarket,providedthat
manufacturingcostsarekeptlowenoughtohave
sustainablemargins.Thefinalstepofthetrendis
“personalizedproduction.”BasedontheIndustry
4.0paradigmandleveragingonnewtechnologies
acrossthecompletevaluechainfromsuppliersto
customers,itispossibletosignificantlyincrease
theflexibilityoftheproductionline,andshorten
productionleadtimes.Thisleadstomoreaffordable
andscalablecustomization.
Thetrendfor“personal”productcustomization
isgrowing,alongwithapreferenceforonline
purchasing.Therefore,currentprocessesneedtobe
adaptedtobemoreflexibleandcustomizable,while
stillprotectinginitialinvestmentsintheproduction
line.Highspeedwirelessinfrastructuresuchas5G
networkscanfacilitatethemodification(required
bycustomizedproducts)ofOEMmachineswith
minimalimpact.
Thedigitizationoffactoryoperationsenabled
byIoTtechnologiespromisestomakethathappen.
Digitaltoolswillbeabletomonitorandcontrolall
toolsofproduction,collectingdatafromthousands
ofsensorstocreateadigitalimageoftheproduct
Figure 2: Evolution of the production paradigm toward Industry 4.0
Productvolumepermodel
Product variety
Mass
production
Mass
customization
Business
model
Craft
production
Personalized
production
1994
1850
1913
1955
1980
PUSH
PULL
Figure 1: The Industry 4.0 maturity index
1 2 3 4 5 6
What is happening?
“Seeing”
Computerization
Value
Connectivity
Digitalization Industry 4.0
Visibility Transparency Adaptability
Predictive
capacity
Why is it happening?
“Understanding”
What will happen?
“Being prepared”
How can an autonomous response be achieved?
“Self-optimizing”
4342 ERICSSON TECHNOLOGY REVIEW ✱ #01 2018 #01 2018 ✱ ERICSSON TECHNOLOGY REVIEW

beingrealized,usuallyreferredtoasa“digital
shadow.”Onceadigitalshadowhasbeencreated
foraphysicalproductandbearsitsspecificDNA,
itispossibletomanufacturethatproductmore
efficientlyandwithahigherdegreeofqualityin
thedigitizedproductionfacility.Inthisway,itis
possibletooptimizethemanufacturingprocess,
detectqualityissuesearlytopreventdefectsatthe
endoftheproductionlineandmakecontinuous
improvements.Itisalsopossibletocarryout
predictiveandpreventivemaintenance.
Thecombinationofwirelesssensorsandhigh-
capacitycommunicationnetworkssuchas4Gand
5Gplaysakeyroleinthiscontext,byenablingdata
collectionfromshop-floorlevel(productionlines)
anddatatransfertocloudsystemsforcontinuous
monitoringandcontrol.
Virtualcontrollersthatcombinecontrol,data
loggingandalarmsintoacloudplatformalsohelp
theprocessofdigitizationandsavecosts,panel
spaceandmaintenanceactivitiescomparedto
traditionalsystems.Theycancontrolawidevariety
ofproductiontoolsandarealsoasolutionfor
remotelylocatedmachinesandportablesystems
thatcanrunstandalone.
Keyautomationtrends
Makinggoodproductsisimportantforthe
successofamanufacturer,butitisnotenoughto
beprofitableandtosustainbusiness.Production
costsmustbelowenoughforasuitablemargin.
Thiscanbeachievedbyincreasinglyimproving
theefficiencyofamanufacturingsystem.
Automationisvitalforthat.Manufacturing
systemsrequireheavyinvestmentsandmustbe
designedsothattheyremainprofitableforthe
longterm.Ifmanufacturersaretoremain
competitiveinanever-changingmarketplace,
theymustcontinuouslyimprovebothproducts
andtheproductionsystems.Virtual
commissioningisthereforenecessaryto
continuouslyupgradeaproductionsystemwith
reasonableincrementalinvestments.This
requiresavirtual(computer-based)environment
thatcansimulateamanufacturingplant.
Virtualcommissioninginvolvesavirtualplant
andarealcontroller.Thesimulatedplantmodel
hastobefullydefinedatthelevelofsensorsand
actuators.Amajorbenefitofthisisthatitreplaces
theneedforrealcommissioningwithrealplants
andcontrollers,whichisveryexpensiveandtime
consuming.Instead,virtualcommissioningallows
fortheidentificationofpossibledesigndefectsand
operationalmistakesbeforeinvestmentsaremadein
physicalplantinfrastructure.Thedigitizationofthe
manufacturingplantallowsitsdesignerstoenhance
theefficiencyoftheproductionprocess,increase
theautomationdensityandoptimizethehandlingof
materialsnecessarytorealizetheproducts.
Digitizationallowstheintroductionofthe
“justinsequence”concept,wherecomponents
andpartsarriveataproductionlineaccordingto
schedule,rightintimeforassembly.Inaddition,it
canenableatrulyleanenterprise,allowingfora
muchricherunderstandingofthecustomerdemand
andtheimmediatesharingofthedemanddata
throughoutcomplexsupplychainsandnetworks.
Smartfactoriescanproduceatafasterratewithless
waste.Industry4.0enablesamuchquickerflowof
customizedproducts.Ithasthepotentialtoradically
reduceinventoriesthroughoutthesupplychain.
Finally,itisimportanttohighlightthe“zero-
defect”concept.Insomecases,relevantpercentages
ofproductioncanendupasscrapbecauseof
manufacturingdefects.A“zero-defect”process
requiresautomaticmonitoringoftheentire
manufacturingprocess,fromthequalityofraw
materialsenteringtheproductionlinetovariances
intoolsandprocessesduringeachproduction
run.Asaclosed-loopsystem,controllersare
immediatelyalertedtoanydefects,andchanges
canbemadeimmediatelytoeliminatethesource
oftheproblem.Theapproachhasthepotentialto
dramaticallyreducescrapbydetectingproduction
errorsinstantly,eliminatingthepropagationof
defectsalongtheprocessstages.Themanufacturing
systemcouldincludeknowledge-basedloops,
providinginformationandfeedbacktootherlevels
ofthemanufacturingchain,tominimizefailuresvia
continuousoptimizationoftheproductionprocess
andthemanufacturingsystem.
Thefactoryofthefuturecouldconsistofflexible
productionislands,abletorealizedifferenttypesof
buildingblocks,withouttherigidityofconveyors
andwithtrulystandardrobotizedworkingstations.
Asaresult,agileshuttlingrobotsareneededto
transferassembledblocksfromoneproduction
islandtoanotherwithouttheneedforphysical
orvirtualrails.
Digitalfactoryelementsandtheroleof5G
Thevirtualplantconceptmakesitpossibleto
carryoutglobalsystemdesign,simulation,
verificationandphysicalmappingatamuchlower
costthanwhatispossiblewithaphysicalplant.To
doso,however,thevirtualplantrequiresnew
kindsofrobotswiththeabilitytoincreasethe
flexibilityoftheglobalproductionsystem.These
robotsneedtobemultipurposeandintelligent
enoughtoadapt,communicateandinteractwith
eachotherandwithhumans,basedonaremote
controlthatcangloballymanageacompletesetof
robotsystems.
High-qualitywirelessconnectivityisessential
tothevirtualplantconcept.Wiredconnectivity,
withitscomplexcabling,wouldnotbefeasiblein
thistypeofever-changingenvironmentduetothe
factthatcableupgradingentailshighoperational
expenditure.Thewirelessconnectivitymust
connectallphysicalelementsofaproductionplant
withmachine(computing)elementsthatareable
tocollectandprocesshugeamountsofdataand/or
withacloudthatisresponsibleforthoseoperations.
Communicationsamongalltheseelementsmust
workinachallengingenvironmentcharacterizedby
electromagneticinterferences,anddistributedover
alargeareathatcouldspanseveralbuildings.While
LTEconnectivityisrobustandcapableenoughto
copewiththatenvironmenttoday,stringentlatency
requirementswillsoondemand5Gconnectivity.
Onceahugeamountofdatahasbeencollected
throughthewirelessconnectivity,itisnecessaryto
usenewmethodstohandle,processandtransform
itintoaformatthatcanbeusedbyhumansor
machinesorboth,andtotapintothepotentialof
cloudcomputing.
Bigdataandanalyticssystemsaretherefore
essentialinthedigitalfactory.Eventually,acyber-
physicalsystemwillbeneededtohandlethe
complexproductionprocess,consistingofIT
systemsbuiltaroundmachines,storagesystemsand
supplies.Alltheaboveleadstofurtherefficiencies
inthefactory,byallowingpreventiveandpredictive
maintenancethatreducesthetimetheplantisoff-
service,minimizingproductiondelaysandavoiding
faults.Oneinterestingeffectoftheefficiency
boostisthereducedenergyconsumption.Process
reengineeringissomethingthatwillbedoneata
lowercostthantoday.
Thechallengeofconnectingrobots
ApreviousarticleinEricssonTechnologyReview
[3]explainedthebenefitofcloudroboticsfor
variousareasofindustry(manufacturing,
agricultureandtransportation)indifferent
logisticscontextssuchasharborsandhospitals.
Therisinglevelofintelligenceinrobotsallows
themtoadapttochangingconditions,whichis
positivefordevelopmentbutsignificantly
increasestheircomplexity.Connectingrobotsand
placingthiscomplexity(intelligence)inacloud
willmakeitpossibleforaffordable,minimal-
infrastructuresmartrobotsystemswithunlimited
computingcapacitytoevolve.Aspartnersinthe
“5GforItaly”program,Ericsson,theSant’Anna
SchoolofAdvancedStudies,Comauand
ZucchettiCentroSistemihavebeencarryingout
researchtogetherintheseareasforthepastthree
years.
Thecurrentindustrialcontrolsystems
architecturehasprovidedastable,secure,and
robustplatformforthepast25years.Butthese
legacysystemsarereachingendoflifeinmany
industrialapplicationssuchasrobotizedcells,and
ongoingmaintenance,andupdatesarebecoming
complicatedandexpensive.Inaddition,legacy
equipmentwasnotbuilttoensureeffectivedata
accesswithinthesesystems,whichseverelylimits
theirpotentialforremotemonitoringandcontrol.
High-speedcommunicationnetworks,wireless

infrastructureandcloudcomputingtechnologies
makeitpossibletoenrichrobotizedplantswithnew
relevantcapabilities,whilereducingcoststhrough
cloudtechnologies.Asaresult,itispossibletocreate
smarterrobotswith“brains”(virtualcontrollers)in
thecloud.The“brain”consistsofaknowledgebase,
programpath,models,communicationsupport
andsoon,effectivelytransferringtheintelligenceof
thecontrollerintoaremotevirtualcontroller.This
approachoffersmanybenefits,including:
〉〉 on-premisescloudcapabilitythatcanreside
alongsidelegacycriticalinfrastructure,allowing
foranevolutionoflegacyservicesandaplatform
fornewservices
〉〉 loweroperatingexpensesasaresultof
maximizingtheperformanceandcapacityof
avirtualizationplatform,whichprovideshigh
reliabilityandperformance
〉〉 faulttolerancetosingleandmultiplesoftware
andhardwarefaults,withminimallossofservice
〉〉 comprehensivefaultmanagement,isolationand
recovery
〉〉 highscalabilityandperformance.
Attheoutset,4GsystemsandWi-Fiwillbeused
toprovidecloudroboticswiththenecessary
connectivity,but5Gisthetargettechnologytruly
capableofdeliveringtheperformanceneededto
supporttheapplicationsofthefuture.
Theproofofconcept(PoC)thatEricsson,
ComauandTIM(TelecomItaliaS.p.A.)have
realizedtogetherisarelevantexample.Illustrated
inFigure3,thePoCconsistsofaworkingcellwith
tworobotsandaconveyor.Thefirstrobotisa
manipulatorthatpicksanobjectandplacesitinfront
ofthesecondrobot,whichemulatessolderingusing
aweldinggun.Thefinalobjectisthenplacedbythe
manipulatorontheconveyortosendittothenext
workcell.InthisPoC,wehavemovedthecontrol
logicthatdrivestheworkingstationresponsible
fortheconcurrentactionsofthetworobotsand
theconveyor.Itwouldnormallyresideinacontrol
cabinetreferredtoasastationPLC(programmable
logiccontroller),butinourPoCwehavemovedittoa
cloudplatform.Movingarelevantpartofthecontrol
tothecloudenablesthevirtualizationofthose
functionalitiesthatcanrunasvirtualmachineson
generalpurposehardware. Figure 4: Virtualization of control in the cloud: steps toward true cloud robotics enabled by 5G
Cloud
Work cell
Station PLC
Robot controller
Robot
Latency 1ms
Latency 5ms
Latency 30ms
With LTE
With 5G
Sensors/actuators
Driver
Control loop
Task control
Task planner
Trajectory planner
Inverse kinematics
Figure 3: Experimenting with cloud robotics in Comau’s industrial automation lab in Turin, Italy
WeusedanindoorLTEnetworkinstallationto
connecttheelementsoftheworkingcellwiththe
cloudserver,whichwaspossiblebecausetheLTE
connectivityensuresafewtensofmillisecondsof
roundtriplatency.AsshowninFigure4,thenext
stepwillbetomovethecontrolfunctionalitiesthat
resideineachrobotcontroller–thetaskplanner,
trajectoryplannerandinversekinematics–tothe
cloudaswell.Thatstep,requiringalatencyofthe
orderof5ms,requires5Gtechnology.Thelaststep
wouldbetomovethecontrolloopoftherobotinto
thecloudaswell.
ThisPoCrepresentsakeyelementofthefactory
ofthefuture,allowingeasierimplementationofnew
controlfeatures,avoidingtheneedfornewPLC
hardwarewhennewactuatorsaredeployed,and
permittingthedeploymentofadifferentlevelof
controlfunctionsonthesameplatform:factory,cell,
actuatorlevel.Cooperativedevicecontrolisalso
possible.
Cooperationbetweenhumansandrobots
Oneofthemostinterestingaspectsofthefourth
industrialrevolutionistheemergenceofhuman-
robotcooperation.Thepresentandfuture
challengeistodeveloprobotsthatcansupport
humanworkersinameaningfulwaytoperform
manipulationandassemblytasksaccordingtoa

Ericsson Technology Review - Issue 1, 2018

Ericsson Technology Review - Issue 1, 2018

Ericsson Technology Review - Issue 1, 2018

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